Training, productivity and wages in Italy

Labour Economics 12 (2005) 557 – 576 www.elsevier.com/locate/econbase

Training, 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 1996–1999. 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; Wages

1. 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 bFinal ConsiderationsQ of this year’s Report to the annual General Meeting, has urged immediate policy responses to stop the loss of competitiveness suffered by the Italian system. One of the key factors behind such a bproductivity gapQ has long been recognized in the lack of ability of the Italian labour force to adapt to the everchanging needs of the global market. * Tel.: +44 1206 874875. E-mail address: [email protected]. 0927-5371/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.labeco.2005.05.007

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However, notwithstanding the complete redesign of the training system carried out in the recent years, Italy’s 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 lowtraining 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 O’Connell, 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 1996–1999. 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 training, using a question which has been consistently asked over time in the Italian Labour Force Survey.

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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 used for the empirical estimation. A simple empirical framework to analyze the impact of training 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 1996–1999 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). Two 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;

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however, it was possible to build up a consistent series because the basic structure of the question was unchanged. The question in 1996–1997 was: bDuring the month before the interview, have you attended any of the following courses?Q1; then, in 1998–1999 it was rephrased as: bDuring the month before the reference week, have you attended any of the following 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. In order to derive a measure of the worker’s 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 1996–1997 the question was: bWhat is the highest level of vocational training achieved during your life?Q; in 1998–1999 it was rephrased as: bDuring your life have you concluded a vocational training course or have you taken part in an on-the job training activity?Q. I have used this question, combined with 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 capital 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 ¼ FLOWit þ ð1  dÞSTOCKi;t1

ð1Þ

where y measures the depreciation rate of the human capital. Since an exact value for y is essentially unknown, I have experimented with various rates of depreciation. The results 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 1996–1997 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 1996–1997). 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 in order to avoid double-counting: although the question refers to the training already concluded, and not still ongoing, 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.

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have chosen a value of y = 0.15, taking the estimates derived in Groot (1998) as a benchmark.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 employed in a different sector. Since I don’t 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 1996–1999. 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 11–17% 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 the database used, given the structural composition of the Italian industry, mainly formed of small and medium enterprises.

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Table 1 Training incidence by sector Rank

ATECO2002

Description

Flow(%)

Stock(%)

1 2 3 4 5 6 7 8 9 10 11

11 8 2 9 12 7 3 5 6 4 1

Education, Health and related Social Services Finance, Banking and Real Estate Energy, Mining and Quarrying Business Services and other Professional Activities Community, Social and Personal Services Transports and Communication Manufacturing Wholesale and Retail Trade Hotels and Restaurants Construction Agriculture

10.74 8.12 5.96 5.79 4.44 3.72 2.65 2.09 1.98 1.79 1.54

43.38 37.29 35.46 32.39 27.26 23.49 22.15 17.96 19.77 17.97 11.91

and 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 (5–digit 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 years, 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 2–digit 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 d’Aosta) 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.

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Table 2 Summary statistics (pooled sample) Variable Proportions Stock of training Flow of training Male employees 15–24 25–34 35–44 45–54 55–64 Degree/postdegree Upper-secondary Vocational Compulsory education Levels Log real value added per employee Log real wage per employee Log capital-labour ratio Log R&D expenditure per employee Average hours worked Average firm size Growth rates Labour productivity Wages Inflow rate Outflow rate

Mean

Std. Dev.

Min.

Max.

0.243 0.039 0.665 0.091 0.280 0.290 0.234 0.114 0.096 0.284 0.072 0.548

0.115 0.036 0.181 0.044 0.068 0.051 0.057 0.058 0.117 0.135 0.046 0.220

0.028 0 0.251 0.005 0.054 0.156 0.097 0 0 0.041 0 0.089

0.680 0.214 1 0.267 0.567 0.517 0.500 0.468 0.468 0.711 0.285 0.941

10.701 9.894 10.823 5.063 40.5 138.9

0.367 0.272 0.712 1.447 3.5 626.1

9.152 8.527 8.435 0 29.4 7.6

14.219 13.742 14.208 9.054 48.9 8137.9

0.028 0.033 0.061 0.109

0.177 0.153 0.163 0.203

 1.474  1.495 0 0

1.218 1.249 1.351 1.638

exacerbated 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. 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. 13

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Table 3 Summary statistics (pooled sample) Variable Proportions Stock of training Flow of training Male employees Age 15–24 Age 25–34 Age 35–44 Age 45–54 Age 55–64 Degree/postdegree Upper-secondary Vocational Compulsory education Levels Log real value added per employee Log real wage per employee Log capital-labour ratio Log R&D expenditure per employee Average hours worked Average firm size Growth rates Labour productivity Wages Inflow rate Outflow rate

High training

Low training

0.331 0.061 0.609 0.083 0.289 0.306 0.236 0.085 0.155 0.347 0.084 0.413

0.155 0.016 0.721 0.098 0.271 0.274 0.233 0.124 0.036 0.222 0.060 0.682

10.771 9.949 10.809 4.976 38.71 226.74

10.632 9.838 10.836 5.150 42.37 49.33

0.043 0.042 0.068 0.099

0.012 0.024 0.053 0.121

and 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 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.

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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-trainingT industries, 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 rate 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), it is assumed that the production function for the economy is represented by a standard Cobb-Douglas: Q ¼ ALa K b

ð2Þ

where 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. 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 two periods. 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. 23 Griliches and Ringstad (1971) list numerous justifications for the value-added specification of the production function. 18

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training has a positive effect on workers’ productivity, effective labour can be written as: L ¼ N U þ cN T

ð3Þ

where N U are untrained workers and N T are trained workers (and we expect g N 1). Substitution of Eq. (3) into (2) yields:
a ð4Þ Q ¼ A N U þ cN T K b which, after some manipulations, can be rewritten as: Q ¼ Að1 þ ðc  1ÞT RAIN Þa N a K b

ð5Þ

where TRAIN = N T /N represents the proportion of trained workers in an industry. The production function can be rewritten in logarithmic form as:24 lnQ ¼ lnA þ aðc  1ÞT RAIN þ alnN þ blnK

ð6Þ

Finally, under the assumption of constant returns to scale, Eq. (6) can be respecified in per-capita terms as: 

Q ln N



  K ¼ lnA þ ð1  bÞðc  1ÞT RAIN þ bln N

ð7Þ

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 NK 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 1996–1999; 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 ¼ a þ b1 T RAIN þ b2 Xit þ eit

ð8Þ

where y it is the outcome of interest (labour productivity or wages), X it the vector of explanatory variables, and qit = f i + u it , i.e. the error term is composed of a time-invariant industry-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-invariant 24

Here I use the approximation ln(1 + x) = x, assuming (g1)TRAIN is small.

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

ð9Þ yi;t  yi;t1 ¼ b1 TRAINit  TRAINi;t1 þ b2 Xit  Xi;t1 þ eit  ei;t1 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 long-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 worker’s dimension (by adding proxies for human capital such as age and education), and in the firm’s dimension (by including percapita 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 FirstDifference 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 timeinvariant 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 T–2 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 SystemGMM 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 (qit Dqi,t1) = 0, where i indexes the industry, and t = 3, 4,. . .,T is the total number of periods in which the industry is present.

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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 extended 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 industries’ heterogeneity both in an observed and in an unobserved dimension. However, endogeneity and 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-step GMM 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 OLS(1)

OLS(2)

FE(1)

Train(%) 0.234 (0.130) 0.424 (0.178) 0.322 Ln(K/N) 0.333 (0.017) 0.330 (0.018) 0.431 Ln(R&D/N) 0.029 (0.009) 0.015 (0.009) 0.016 Ln(Hours/N) 0.919 (0.169) 0.365 (0.318) 0.236 Inflow(%) 0.044 (0.073) 0.045 (0.059) 0.018 Outflow(%) 0.078 (0.062) 0.066 (0.049) 0.027 Male 0.381 (0.158) Age 25–34 0.171 (0.408) Age 35–44 0.404 (0.396) Age 45–54 0.470 (0.048) Age 55–64 0.193 (0.429) Degree/post 0.391 (0.327) Uppersec 0.238 (0.217) Vocational 0.168 (0.435) R2 0.428 0.669 0.407 NT 633 633 633

FE(2)

Model 2

OLS(1)

OLS(2)

FE(1)

(0.167) 0.349 (0.172) DTrain(%) 0.314 (0.132) 0.343 (0.144) 0.383 (0.027) 0.431 (0.028) DLn(K/N) 0.314 (0.025) 0.317 (0.026) 0.399 (0.009) 0.017 (0.010) DLn(R&D/N) 0.006 (0.008) 0.005 (0.008) 0.003 (0.346) 0.315 (0.364) DLn(Hours/N) 0.124 (0.281) 0.102 (0.299) 0.451 (0.047) 0.007 (0.048) DInflow(%) 0.079 (0.034) 0.087 (0.035) 0.133 (0.039) 0.024 (0.039) DOutflow(%) 0.040 (0.031) 0.045 (0.031) 0.071 0.152 (0.251) DMale 0.441 (0.202) 0.393 (0.389) DAge 25–34 0.005 (0.318) 0.404 (0.381) DAge 35–44 0.343 (0.303) 0.127 (0.396) DAge 45–54 0.202 (0.318) 0.395 (0.458) DAge 55–64 0.176 (0.363) 0.022 (0.433) DDegree/post 0.527 (0.344) 0.294 (0.238) DUppersec 0.466 (0.195) 0.060 (0.408) DVocational 0.515 (0.329) 0.413 R2 0.283 0.292 0.411 633 NT 456 456 456

FE(2) (0.159) 0.376 (0.159) (0.032) 0.392 (0.032) (0.009) 0.002 (0.009) (0.338) 0.354 (0.343) (0.038) 0.127 (0.038) (0.032) 0.065 (0.035) 0.558 (0.224) 0.028 (0.363) 0.337 (0.329) 0.374 (0.351) 0.270 (0.413) 1.013 (0.393) 0.588 (0.217) 0.495 (0.372) 0.451 456

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Model 1

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.

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inputs to changes in demand, as outlined above. Henceforth, the GMM-estimator has been implemented 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 pvalue of 0.24. Now I’ll 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 dAshenfelter’s dipT. All the other variables are conventionally signed. Turning to the results 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 engaging

30

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)

0.527 (0.082) 0.451 (0.177) 0.431 (0.293) 0.348 (0.057) 0.134 (0.057) 0.026 (0.016) 0.037 (0.015) 0.199 (0.424) 0.710 (0.455) 0.139 (0.137) 0.018 (0.075) 0.303 (0.117) 0.059 (0.047) 0.236 0.004

0.502 (0.079) 0.408 (0.183) 0.349 (0.282) 0.336 (0.055) 0.131 (0.055) 0.021 (0.017) 0.035 (0.014) 0.293 (0.398) 0.732 (0.464) 0.132 (0.135) 0.039 (0.085) 0.299 (0.113) 0.054 (0.040) 0.166 0.005

456

456

0.313 (0.210)

0.334 (0.203)

0.516 (0.088) 0.317 (0.168)

0.153 (0.068)

0.317 (0.056)

0.246 (0.050)

0.505 (0.084) 0.383 (0.147) 0.412 (0.279) 0.254 (0.044)

0.006 (0.029)

0.006 (0.023)

0.013 (0.018)

0.013 (0.016)

0.359 (0.583)

0.942 (0.558)

0.432 (0.398)

0.526 (0.420)

0.062 (0.055)

0.006 (0.087)

0.166 (0.134)

0.166 (0.145)

0.095 (0.061)

0.060 (0.065)

0.359 (0.133)

0.344 (0.121)

0.315 0.001 0.441 456

0.306 0.001 0.955 456

0.292 0.003

0.250 0.003

456

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Ln(vadd/N)t1 Train(%) Train(%)t1 Ln(K/N) Ln(K/N)t1 Ln(R&D/N) Ln(R&D/N)t1 Ln(Hours/N) Ln(Hours/N)t1 Inflow(%) Inflow(%)t1 Outflow(%) Outflow(%)t1 Hansen test AR(1) test AR(2) test NT

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.

571

572

Table 6 Wage equation estimates OLS(1)

Train(%) Ln(K/N) Ln(R&D/N) Ln(Hours/N) Inflow(%) Outflow(%) Male Age 25–34 Age 35–44 Age 45–54 Age 55–64 Degree/post Uppersec Vocational R2 NT

0.059 0.171 0.017 0.959 0.021 0.070

0.236 633

OLS(2) (0.112) (0.014) (0.008) (0.145) (0.062) (0.053)

0.459 0.177 0.001 0.537 0.080 0.033 0.499 0.061 0.676 0.570 0.261 0.329 0.194 0.352 0.468 633

FE(1)

FE(2)

Model 2

(0.167) 0.203 (0.172) 0.215 (0.176) DTrain(%) (0.017) 0.342 (0.028) 0.339 (0.029) DLn(K/N) (0.008) 0.003 (0.010) 0.003 (0.010) DLn(R&D/N) (0.299) 0.245 (0.355) 0.273 (0.373) DLn(Hours/N) (0.056) 0.085 (0.048) 0.073 (0.135) DInflow(%) (0.047) 0.017 (0.040) 0.010 (0.041) DOutflow(%) (0.148) 0.030 (0.258) DMale (0.383) 0.429 (0.399) DAge 25–34 (0.372) 0.549 (0.391) DAge 35–44 (0.383) 0.615 (0.406) DAge 45–54 (0.403) 0.351 (0.469) DAge 55–64 (0.307) 0.001 (0.444) DDegree/post (0.204) 0.207 (0.245) DUppersec (0.408) 0.179 (0.418) DVocational 0.331 0.337 R2 633 633 NT

OLS(1) 0.253 0.184 0.005 0.164 0.030 0.044

0.168 456

OLS(2)

FE(1)

FE(2)

(0.123) 0.305 (0.134) 0.319 (0.157) 0.287 (0.156) (0.023) 0.187 (0.024) 0.234 (0.032) 0.225 (0.031) (0.008) 0.004 (0.008) 0.003 (0.009) 0.004 (0.009) (0.262) 0.136 (0.280) 0.435 (0.334) 0.343 (0.336) (0.032) 0.025 (0.033) 0.004 (0.037) 0.001 (0.037) (0.029) 0.049 (0.029) 0.066 (0.034) 0.071 (0.034) 0.341 (0.189) 0.480 (0.219) 0.018 (0.297) 0.169 (0.356) 0.386 (0.283) 0.363 (0.323) 0.497 (0.297) 0.567 (0.344) 0.126 (0.339) 0.006 (0.405) 0.522 (0.322) 0.845 (0.386) 0.514 (0.182) 0.697 (0.213) 0.709 (0.308) 0.787 (0.365) 0.175 0.246 0.306 456 456 456

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 / Labour Economics 12 (2005) 557–576

Model 1

Table 7 Wage equation estimates Model 1

GMM-FD

GMM-SYS(1)

GMM-SYS(2)

GMM-SYS(3)

GMM-SYS(4)

GMM-SYS(5)

0.299 (0.117) 0.181 (0.154) 0.375 (0.228) 0.194 (0.052) 0.053 (0.029) 0.019 (0.021) 0.031 (0.011) 0.588 (0.315) 0.664 (0.369) 0.032 (0.134) 0.003 (0.053) 0.248 (0.100) 0.012 (0.029) 0.484 0.006 . 456

0.314 (0.111) 0.123 (0.160)  0.412 (0.260) 0.185 (0.060)  0.046 (0.030) 0.017 (0.022) 0.023 (0.010)  0.607 (0.454)  0.685 (0.408)  0.016 (0.123) 0.008 (0.056) 0.264 (0.112) 0.016 (0.031) 0.382 0.007 . 456

0.199 (0.197)

0.021 (0.167)

0.331 (0.118) 0.091 (0.128)

0.092 (0.061)

0.154 (0.043)

0.153 (0.045)

0.341 (0.108) 0.158 (0.124) 0.434 (0.206) 0.166 (0.041)

0.031 (0.027)

0.002 (0.019)

0.005 (0.017)

0.009 (0.015)

 0.144 (0.562)

1.022 (0.409)

0.738 (0.297)

0.825 (0.286)

 0.092 (0.053)

0.053 (0.101)

0.022 (0.134)

0.007 (0.116)

 0.062 (0.063)

0.027 (0.067)

0.248 (0.114)

0.244 (0.113)

0.373 0.002 0.814 456

0.259 0.001 0.609 456

0.463 0.004

0.638 0.004

. 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 / Labour Economics 12 (2005) 557–576

Ln(W/N)t1 Train(%) Train(%)t1 Ln(K/N) Ln(K/N)t1 Ln(R&D/N) Ln(R&D/N)t1 Ln(Hours/N) Ln(Hours/N)t1 Inflow(%) Inflow(%)t1 Outflow(%) Outflow(%)t1 Hansen test AR(1) test AR(2) test NT

573

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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 coefficient 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 always greater than the effect on wages (for example, in the full-dynamic specification, the coefficient 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 individuallevel 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, still lacking for Italy, will provide useful results in terms of comparability between private and social returns to postschooling human capital.

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and 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 I am deeply indebted to my supervisor, Amanda Gosling, for her generous and 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. PTA–030–2003–00812, is gratefully acknowledged. The usual disclaimer applies.

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