Training, Productivity and Wages

Transcription

1 Training, Productivity and Wages Gabriella Conti Department of Economics University of Essex June 30, 2004 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 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. Wivenhoe Park, Colchester CO43SQ, United Kingdom. gconti@essex.ac.uk. 1

2 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 Final Considerations 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 "productivity gap" has long been recognized in the lack of ability of the Italian labour force to adapt to the everchanging needs of the global market. 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, 1994 and 1996; Acemoglu and Pischke, 1998 and 1999) 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 mar- 2

3 kets, 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 O Connell, 2001); longitudinal data with repeated training information have become available only recently (Black and Lynch, 2001; Dearden et a., 2000; Ballot et a., 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 3

4 on training and measures of corporate productivity covering all sectors of the Italian economy for the years 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. 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 4

5 returns. Finally, following Dearden et a. (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 II 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 III. Section IV 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 a. (2000). I have used the 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 5

6 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 Italian Labour Force Survey is a sample survey carried out with a detailed questionnaire every quarter (in the months of January, April, July and October) since Around 75,000 households are interviewed every three months, for a total of circa 200,000 individuals. From the LFS I derive information on training, personal characteristics of the individuals (sex, age), measures of their skills (education and occupation), and job characteristics (hours of work). My sample includes all employed men and women aged more than 15 (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; however, it was possible to build up a consistent series because the basic structure of the question was unchanged. The question in was phrased in the following terms: During the month before the interview, have you attended any of the following courses? ; then, in it was rephrased as: During 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?. 1 One drawback with this training measure is that it only measures the flow of training that occurred in the previous month, 1 In the 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. 6

7 rather than measuring the accumulated stock of training. In order to take into account the worker s stock of knowledge, 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 , Italian workers were asked the following question: What is the highest level of vocational training achieved during your life? ; in it was rephrased as: During yourlifehaveyou concluded a vocational training course or have you taken part in an on-thejob training activity?. I have used this question, combined with the previous information on current training, to calculate the stock of training in each year. The second source used in this paper is AIDA (Analisi Informatizzata delle Aziende). This is a private database, 2 which provides accounting information from the balance sheets of all Italian companies with an annual turnover higher than one million Euros. 3 The original sample contained 189,059 firms, covering a ten-year period from 1993 to An accurate work of data selection has led to the exclusion of 82,413 firms due to incomplete balance sheets, and of further 54,886 observations for lack of consistency between specific budget items. 4 Henceforth, data limitations have confined the sample to a total of 51,760 companies, covering all the sectors of the Italian economy for the years From the AIDA database I have derived information on value added, 2 It is provided by Bureau van Dijk. 3 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. 4 For a smaller subset it has been possible to interpolate the missing values on wage bills, due to the presence of information on total labour costs. The decision to follow this procedure has arisen from the necessity to avoid the loss of representativeness of the sample, given the relevant weight that these firms have in their sector due to their high dimensionality. 7

8 wages, capital stock, R&D expenditures, employment and firm size. Real values have been obtained by deflating the nominal measures with the producer priceindexesforthedifferent years provided by ISTAT (the Central Statistics Institute). The data drawn from the two datasets have then been aggregated into proportions and averages at regional (over the 20 Italian regions) 5 and sectoral level, according to the ATECO2002 classification in 12 sectors, and then merged. The main reason for following this procedure 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 data drawn from the Labour Force Survey are reliable only at a higher level of aggregation (12 sectors). Furthermore, aggregation of the data also at a regional level allows me 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 a. (2000) aggregation allows to capture the within-industry spillovers that would be left out in case of a firm- or individuallevel analysis, although the advantages arising from this methodology have to be weighted against the possible problems due to aggregation bias (see Grunfeld and Griliches, 1960). Table 1 illustrates the incidence of training across sectors, ranking each of them by its propensity to train. It s readily seen that high-training industries 6 5 The final number of regions was reduced to 19, due to the lack of information on one of the smaller regions in the North (Val d Aosta) in the Labour Force Survey. 6 Here high-training industries are defined as those with a training incidence above the sample median, which equals

9 are those providing services of different nature; this seems pretty obvious, given that this kind of business crucially relies on the role of human resources. The high ranking of Finance, Banking and Real Estate comes at no surprise, given the intensive use of computers and IT equipments in these sectors. Above the sample median also rank industries in the Energy, Mining and Quarrying sector, which is to be expected, since these industries include a lot of specialized equipment and severe safety requirements. Finally, the low ranking of industries in Transport & 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. Summary statistics for the variables used in this paper are provided in Table 2. [Table 1 and 2 about here] 3 The Model Following a modelling strategy consolidated in the literature (see Dearden et a., 2000), it is assumed that the production function for the economy is represented by a standard Cobb-Douglas: Q = AL α K β (1) 9

10 where Q is value added, 7 L is effective labour, K is capital, and A is a Hicksneutral technology parameter. Following Dearden et a. (2000), under the assumption that training has a positive effect on workers productivity, effective labour can be written as: L = N U + γn T (2) where N U are untrained workers and N T are trained workers (and we expect γ>1). Substitution of equation (2) into (1) yields: Q = A N U + γn T α K β (3) which, after some manipulations, can be rewritten as: Q = A (1 + (γ 1) TRAIN) α N α K β (4) where STOCK = N T /N represents the proportion of trained workers. The production function can be rewritten in logarithmic form as: 8 ln Q =lna + α (γ 1) TRAIN + α ln N + β ln K (5) 7 Griliches and Ringstad (1971) list numerous justifications for the value-added specification of the production function. 8 Here I use the approximation ln(1 + x) =x, assuming (γ 1) TRAIN is small. 10

11 Finally, under the assumption of constant returns to scale, equation (5) can be respecified in per-capita terms as: ln µ µ Q K =lna +(1 β)(γ 1) TRAIN + β ln + ε (6) N N where the dependent variable, labour productivity, is measured as the natural logarithm of real value added per employee from the balance sheets, STOCK µ K is the proportion of trained workers in an industry, and ln is measured as N 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 Boon and van der Eijken (1998), I 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, 9 taking into account depreciation, according to the Perpetual Inventory Method: TRAIN it = FLOW it +(1 δ) STOCK i,t 1 (7) where δ measures the depreciation rate of the human capital. Since an exact value for δ is essentially unknown, I have experimented with various rates of depreciation. The results were not sensitive to different values, within a plausible range 5%-25%, so I have chosen a value of δ =0.15. Moreover, I have controlled for turnover to take into account of the loss in human capital arising from 9 The availability of LFS data for 1995 allowed me to obtain a precise measure of the stock of human capital also for

12 separations. Following Dearden et a. (2000), I have firstly estimated the above production function with constant returns to scale; 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: y it = α + β 1 TRAIN + β 2 X it + ε it (8) where y it is the outcome of interest (labour productivity or wages), X it the vector of explanatory variables, and ε it = f i +u it, i.e. the error term is composed of a time-invariant firm-specific effect, and a time-varying white noise. Equation (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 firm-specific effect. To deal with this potential source of bias, a first-difference version of equation (8) has been estimated: y i,t y i,t 1 = β 1 (TRAIN it TRAIN i,t 1 )+β 2 (X it X i,t 1 )+u it u i,t 1 (9) This equation relates productivity growth to the change in training intensity. As argued in Barrett et a. (1998), 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. 12

13 To ensure greater flexibility, an alternative modelling specification has been adopted, allowing for non-constant returns to scale. Following Bartel (1995), the framework for this estimation assumes that the relationship between output and inputs has the following Cobb-Douglas structure: Q = AEL γ K β (10) where all the variables are defined as above. Effective labour now consists of the number of workers employed (RL, or reported labour), and the stock of human capital that the workforce has received, here measured by the number of trained workers (T ). The relationship between these variables is defined as follows: EL = RL (1 + λt ) (11) As before, since trained workers are expected to be more productive than untrained workers, λ is greater than zero, and effective labour is greater than reported labour. Substitution of equation (11) into (10) yields: Q = A (RL (1 + λt )) γ K β (12) Dividing through by reported labour, the per-capita form can be written as: Q RL = AKβ RL γ 1 (1 + λt ) γ (13) 13

14 which can be respecified in the following logarithmic form: µ Q ln =lna + β ln K +(γ 1) ln RL + γλt + ε (14) RL Equation (14) is the third model which has been estimated, in the dual form of production function and wage regression. As above, since the estimation of equation (14) could produce biased estimates of the returns to training due to unobserved heterogeneity, a firstdifferenced version has been also fit into the data: µ µ Qt Qt 1 ln ln = β (ln K t ln K t 1 )+(γ 1) (ln RL t ln RL t 1 )+γλ(t t T t 1 )+ε t ε t 1 RL t RL t 1 (15) where the underlying assumption is that productivity growth is fostered by the change in the stock of human capital, rather than by the flow. In addition, in order to avoid omitted variable bias (and hence overestimate the true returns to training), in the empirical estimation of each of the above models 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 expenditures in R&D as a proxy for the rate of innovation); I have also controlled for gender, working hours, and turnover rate. Then, time dummies have been included to control for time-varying effects, such as the impact of 14

15 technological progress or some other unobserved factor linked to the business cycle. Finally, several estimation techniques have been applied: apart from the already mentioned linear and fixed effect estimators, also standard GMM and System-GMM have been implemented. The GMM handles not only unobserved heterogeneity, but also potential endogeneity of training. The standard GMM first-difference the variables, to eliminate time-invariant specificeffects, and uses lagged variables in levels as instruments to correct for endogeneity. Blundell and Bond (1998 and 1999), however, show that the lagged levels of a series provide weak instruments for the first differences. They propose to take into account additional moment conditions, adding (T-2) equations in levels with variables in differences as instruments. This so-called System-GMM is the last estimation technique adopted. However, a word of caveat before proceding to the discussion of the results: the relatively short time structure of the panel, and the consequent lack of degrees of freedom, severely hampers the efficiency of the estimation procedure in my case. 4 The Results Table 3 presents the estimation results for the productivity regressions. 10 In Model 1, training is measured in terms of the proportion of workers who have accumulated post-schooling skills during their working life. First I have estimated the OLS as a reference. Training has a positive and significant effect 10 In this section we focus on the discussion of the results for the variable of interest. Full tables with all the estimated coefficients are available from the author upon request. 15

16 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 it is driven to insignificance - and it also exhibits a negative sign - after controlling for turnover, workers observed characteristics (sex and age) and for skills (education). Fixed effect estimates uncover a positive impact of training on labour productivity, which remains significant also after conditioning upon the full set of controls. The estimation results for the first-difference version of this model confirm the robustness of this finding: the change in the stock of accumulated human capital has a positive effect on labour productivity growth, although the level of significance never falls below 10%. The same findings are confirmed when the restriction of constant returns to scale is relaxed. [Table 3 about here] The estimation results for the GMM estimation are presented in Table 4. Training has no significant effect on productivity when treated as endogenous. However, these results are likely to be a consequence of the short time structure of the panel, which may cause a weak instrument problem. [Table 4 about here] Table 5 presents the estimation results for the wage regressions. Training has a positive and significant impact on wages only in the first column: after controlling for observed characteristics, the coefficient is driven to insignificance. The within group estimates recover some of the effect, but still the coefficient on training remains insignificant. These results are confirmed in the specification 16

17 which allows for non-constant returns to scale, and in both the first-differenced models. 11 [Table 5 about here] Finally, Table 6 presents the estimation results for the GMM estimation of the effect of training on wages. When taking endogeneity into account, the estimated coefficients are still not significant, and turn out to be negative. [Table 6 about here] To summarize the results, training seems to have a positive and significant effect on the level of productivity. This effect disappears when controlling for observed heterogeneity with the standard linear techniques, and when implementing the GMM estimator. It still persists when unobserved heterogeneity is taken into account, and it is robust to the first-difference specification. The effect of training on wages never achieves significance at a conventional level, if we exclude the basic model estimated with standard linear techniques. 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 and wages from AIDA into an industry-level 11 This finding also emerges in a recent paper by Arulampalam et a. (2004). 17

18 panel, covering the years from 1996 to 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 modelling specifications and estimation strategies. No such effect has been uncovered for wages. This seems suggestive of the fact that firms do actually reap more of the returns. 18

19 References [1] Acemoglu, D. and J.-S. Pischke (1998), Why do firms train? Theory and evidence, Quarterly Journal of Economics, 113(1), , also available as NBER Working Paper, n [2] Acemoglu, D. and J.-S. Pischke (1999a), Beyond Becker: Training in imperfect labour markets, Economic Journal, 109, F112-F142. [3] Acemoglu, D. and J.-S. Pischke (1999b), The structure of wages and investment in general training, Journal of Political Economy, 107(3), , also available as NBER Working Paper, n [4] Arulampalam, W., A.L. Booth and M.L. Bryan (2004), Training in Europe, ISER working paper [5] Ballot, G., F. Fakhfakh and E. Taymaz (2001), Firms human capital, R&D and performance: a study on French and Swedish Firms, Labour Economics, 8, [6] Barrett, A. and P. O Connell (2001), Does Training Generally Work? The Returns to In-Company Training, Industrial and Labour Relations Review, 54(3), , also available as IZA Discussion Paper, n.51. [7] Bartel, A.P. (1994), Productivity Gains from the Implementation of Employee Training Programs, Industrial Relations, 33(4), , also available as NBER Working Paper, n

20 [8] Bartel, A.P. (1995), Training, Wage Growth, and Job Performance: Evidence from a Company Database, Journal of Labor Economics, 13(3), [9] Bassi, L., P. Harrison, J. Ludwig and D. McMurrer (2001), Human Capital Investments and Firm Performance, mimeo, Human Capital Dynamics, Bethesda. [10] Bishop, J.H. (1994), The Impact of Previous Training on Productivity and Wages, in L.M. Lynch (ed.), Training and the Private Sector. International Comparisons, NBER Series in Comparative Labour Markets, Chicago: University of Chicago Press, [11] Black, S.E. and L.M. Lynch (1995), Beyond the Incidence of Training: Evidence from a National Employers Survey, NBER Working Paper, n [12] Black, S.E. and L.M. Lynch (1996), Human Capital Investments and Productivity, American Economic Review (Papers & Proceedings), 86(2), [13] Black, S.E. and L.M. Lynch (2001), How to Compete: The Impact of Workplace Practices and Information Technology on Productivity, The Review of Economics and Statistics, 83, [14] Blundell, R. and S. Bond (1998), Initial conditions and moment restrictions in dynamic panel data models, Journal of Econometrics, 87,

21 [15] Blundell, R. and S. Bond (1999), GMM estimation with persistent panel data: an application to production function, IFS Working Paper, n.99/4. [16] Boon, M. and B. van der Eijken (1998), Employee training and productivity in Dutch manufacturing firms, Statistics Netherlands, vol.13. [17] Brunello, G. (2002), Is Training more frequent when wage compression is higher? Evidence form 11 European countries, CESifo Working Paper, n.637(4). [18] Dearden, L., H. Reed and J. Van Reenen (2000), Who Gains when Workers Train? Training and Corporate Productivity in a Panel of British Industries, IFS Working Paper, n.00/01. [19] Grunfeld, D. and Z. Griliches (1960), Is Aggregation necessarily bad?, The Review of Economics and Statistics, XLII(1), [20] Lynch, L.M. (1994), Introduction, in L.M. Lynch (ed.), Training and the Private Sector. International Comparisons, NBER Series in Comparative Labour Markets, Chicago: University of Chicago Press, [21] OECD (2002), Economic Outlook, Paris. [22] Stevens, M. (1994a), Labour contracts and efficiency in on-the-job training, Economic Journal, 104, [23] Stevens, M. (1994b), A theoretical model of on-the-job training with imperfect competition, Oxford Economic Papers, 46,

22 [24] Stevens, M. (1994c), An Investment Model for the Supply of Training by Employers, Economic Journal, 104, [25] Stevens, M. (1996), Transferable training and poaching externality, in A.L. Booth and D.J. Snower (eds.), Acquiring Skills. Market failures, their symptoms and policy responses, Cambridge University Press: Cambridge. [26] Zwick, T. (2002), Continuous Training and Firm Productivity in Germany, ZEW Discussion Paper, n

23 Table 1: Training incidence by sector Rank ATECO2002 Description % 1 11 Education, Health & related Social Services Finance, Banking & Real Estate Public Administration, Defence & Social Insurance Business Services & other Professional Activities Community, Social and Personal Services Energy, Mining and Quarrying Transports and Communication Manufacturing Wholesale & Retail Trade Hotels & Restaurants Construction Agriculture

24 Table 2: Summary Statistics (Pooled Sample) Variable Mean Std. Dev. Min. Max. proportions stock of training male employees degree/postdegree upper-secondary vocational lower-secondary elementary no 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

25 Table 3: Production Function Estimates Model 1 OLS (1) OLS(2) FE(1) FE(2) Model 2 OLS(2) FE(2) StockTrain(%) 0.467*** *** 0.278** StockTrain(%) 0.186* 0.193* Std. Err Std. Err R 2 (0.343) (0.623) (0.175) (0.234) R 2 (0.317) (0.359) Model 3 OLS (1) OLS(2) FE(1) FE(2) Model 4 OLS(2) FE(2) StockTrain(N) 0.169*** *** 0.056** StockTrain(N) 0.042** 0.039* Std. Err Std. Err R 2 (0.361) (0.631) (0.371) (0.409) R 2 (0.401) (0.444) Dependent variable: log(value added per worker) in Model 1 and Model 3, change in log (value added per worker) in Model 2 and Model 4. All models include year dummies. Models OLS(2) also includes region and sector dummies. In models OLS(1) and FE(1) only capital, hours worked and R&D are included as controls. Models OLS(2) and FE(2) include the full set of controls ( sex, age, education and turnover). Level of significance: ***:1%; **:5%; *:10%. 25

26 Table 4: Production Function Estimates Model 1 GMM-FD(1) GMM-FD(2) GMM-SYS(1) GMM-SYS(2) StockTrain(%) Std. Err Hansen test (0.968) (0.526) (0.904) (0.793) AR(1) test (0.527) (0.127) (0.021) (0.098) AR(2) test (0.910) (0.152) (0.577) (0.464) Model 3 GMM-FD(1) GMM-FD(2) GMM-SYS(1) GMM-SYS(2) StockTrain(N) Std. Err Hansen test (0.999) (0.457) (0.781) (0.574) AR(1) test (0.330) (0.167) (0.118) (0.136) AR(2) test (0.384) (0.168) (0.799) (0.339) Dependent variable: log(value added per worker). All models include year dummies. Model GMM-SYS(2) also includes region and sector dummies. In models GMM-FD(1) and GMM-SYS(1) training, capital, hours worked and R&D are treated as endogenous. In models GMM-FD(2) and GMM-SYS(2) all the variables are treated as endogenous. p-values are reported for AR(1), AR(2) and Hansen tests. Level of significance: ***:1%; **:5%; *:10%. 26

27 Table 5: Wage Equation Estimates Model 1 OLS (1) OLS(2) FE(1) FE(2) Model 2 OLS(2) FE(2) StockTrain(%) 0.247** StockTrain(%) Std. Err Std. Err R 2 (0.128) (0.448) (0.207) (0.221) R 2 (0.092) (0.240) Model 3 OLS (1) OLS(2) FE(1) FE(2) Model 4 OLS(2) FE(2) StockTrain(N) 0.125*** StockTrain(N) Std. Err Std. Err R 2 (0.158) (0.477) (0.391) (0.406) R 2 (0.293) (0.396) Dependent variable: log(wage per worker) in Model 1 and Model 3, change in log (wage per worker) in Model 2 and Model 4. All models include year dummies. Models OLS(2) also includes region and sector dummies. In models OLS(1) and FE(1) only capital, hours worked and R&D are included as controls. Models OLS(2) and FE(2) include the full set of controls ( sex, age, education and turnover). Level of significance: ***:1%; **:5%; *:10%. 27

28 Table 6: Wage Equation Estimates Model 1 GMM-FD(1) GMM-FD(2) GMM-SYS(1) GMM-SYS(2) StockTrain(%) Std. Err Hansen test (0.993) (0.422) (0.957) (0.930) AR(1) test (0.567) (0.766) (0.083) (0.004) AR(2) test (0.729) (0.425) (0.933) (0.566) Model 3 GMM-FD(1) GMM-FD(2) GMM-SYS(1) GMM-SYS(2) StockTrain(N) Std. Err Hansen test (0.988) (0.451) (0.771) (0.817) AR(1) test (0.295) (0.159) (0.016) (0.015) AR(2) test (0.893) (0.470) (0.896) (0.394) Dependent variable: log(value added per worker). All models include year dummies. Model GMM-SYS(2) also includes region and sector dummies. In models GMM-FD(1) and GMM-SYS(1) training, capital, hours worked and R&D are treated as endogenous. In models GMM-FD(2) and GMM-SYS(2) all the variables are treated as endogenous. p-values are reported for AR(1), AR(2) and Hansen tests. Level of significance: ***:1%; **:5%; *:10%. 28