To identify knowledge spillovers, we estimate equation (7). In this case, we compare firms that hired skilled workers from FONTAR beneficiaries (i.e. the knowledge carriers) with firms that did not. In this estimation, the dummy variable that controls for whether a firm has been hiring skilled workers from any other firms becomes much more relevant.23 In fact, because the hiring of skilled workers might improve a firm’s performance per se, without controlling for this factor we might confound spillovers for improvements due to better matching between worker skills and the firm’s needs.
The results in table 7 show that firms that hired the knowledge carriers actually improved their performance in several dimensions. In particular, receiving firms increased employment (3.7%), probability of exports (1.7pp), value of exports (9.9%), survival probability (0.7%), and the average wage they pay to their employees (0.9%), with magnitudes that are in general lower than
23 We do this by including a dummy variable that takes value one the year after the firm hired skilled workers and so thereafter.
Although this control variable was also included in the previous estimations, in this case it is clearly more relevant.
those obtained by FONTAR firms, but still relevant and statistically significant. From the last two columns of Table 7 we observe that the increase in wages is mostly due to an increase in the wage of workers that were already in the receiving firms rather than the wage of the newly hired skilled workers. This finding reveals that the increase in wages is due to an improvement in productivity, rather than to a change in the skill composition. These results confirm the hypothesis that the knowledge acquired through the exposure to a FONTAR project has a productive value that goes beyond the firm directly supported by the program.
[Table 7 here]
An additional source of identification of the spillover effect is the fact that not every firm hired the same number of knowledge carriers. We then explore how the spillover effects vary according to the share of knowledge carriers to total workers in receiving firms.
Table 8 shows similar results to those in Table 7. However, instead of a dummy variable identifying receiving firms, the main explanatory variable is a continuous variable that measures the ratio between the number of knowledge carriers received with respect to the number of workers at the moment of receiving the knowledge carriers. Therefore, this variable measures how the spillover effects react to changes in the intensity of the knowledge diffusion. As expected, the more knowledge carriers received with respect to the number of workers in the firms, the higher the spillover effect ceteris paribus.
[Table 8 here]
Moreover, given that we observe receiving firms each year after they hire the knowledge carrier, we can estimate the way in which the spillover effect takes place in time. Table 9 shows these estimates. They provide us with another robustness check to previous findings. In general, the
accumulative spillover is increasing in time for all outcome variables under analysis. Of particular interest is the dynamics of the effects on the productivity and skill composition terms (Figure 2 and 3). In the former case, the spillover effect increases over time at a growing rate, as shown by the increasing coefficients of the treatment dummy variables.
[Table 9 here]
Finally, we can test whether the effect of hiring a knowledge carrier depends on the level of competition of the sector the firm belongs to. Table 10 shows that the effects tend to be higher for receiving firms in competitive markets. Results also reveal that the knowledge generated through the FONTAR projects seems to be general instead of specific to a particular firm or industry. That is, knowledge coming with these new hires is general enough to be applied in different firms and to overcome technical barriers between different industries.
[Table 10 here]
Summarizing, in terms of our analytical framework our results clearly reject hypothesis two and three and provide evidence in favor of hypothesis one. In fact, we find that new relevant productive knowledge is generated and diffused through labor mobility. The positive effects of the FONTAR program on the long-term performance of supported firms (A>0) confirms that an exogenous knowledge shock for non-participants actually occurred. We then find that this new productive knowledge is applicable and beneficial to non-participant firms that access to it through labor mobility, as shown by their improved long-term performance (D>0). Finally, our results also support hypothesis four as the knowledge spillovers are higher in industries with a low concentration of firms. At the worker level, our findings show that a wage premium is paid to skilled workers exposed to the program either by participant (to retain, B>0) or
non-reference. These findings further confirm the hypothesis that the productive knowledge generated through the innovation program has recognizable market value and is more extensively diffused in less concentrated industries.
6. Conclusions
The goal of this paper was to study the effects of knowledge spillovers on firms’ performance and workers’ wages. For this purpose, we used the participation in an innovation support program as an exogenous shock to the knowledge stock of non-participant firms. We pinpointed the knowledge diffusion process by tracking the mobility of skilled workers among firms based on a 16-years employer-employee panel dataset. We introduced a simple analytical framework that outlines a set of hypothesis to be tested in our empirical analysis and to guide the interpretation of our findings.
To test these hypotheses, we organized our empirical analysis at two levels: firm and worker level effects. At the firm level, we estimated both the effects of receiving skilled workers that previously worked in a firm that participated in the innovation program —knowledge carriers — and the effects of receiving the FONTAR support on different measures of firm performance. At the worker level, we estimated the effect on wages of staying at the participating firm and the effect of moving to non-participant firms.
We found strong and robust evidence in favor of positive knowledge spillovers through labor mobility. In fact, we found that firms that hired knowledge carriers improved their performance after hiring them. They increased their size in terms of number of employees, their probability of exporting, the value of their exports, their survival probability, and the average wages they pay.
Consistent with the hypothesis that effects are caused by newly acquired productive knowledge,
we also found that these effects were driven by an improvement in firm-level productivity.
At the worker level, our results are also consistent with the existence of knowledge spillovers. In particular, our findings show that skilled workers exposed to the FONTAR program received a wage premium, whether they stayed at the beneficiary firm or they moved to another firm. These results confirm that skilled workers acquired valuable productive knowledge and that firms were willing to pay to either retain or acquire such knowledge depending on the level of competition of their market of reference. More specifically, in relatively less concentrated markets non-beneficiary firms were willing to pay a wage premium to acquire such workers higher than the wage premium beneficiary firms would pay to retain them. However, when the market was concentrated, beneficiary firms were willing to pay a higher premium than non-beneficiaries to prevent these workers from being hired by a competitor who could have threatened their market position.
In synthesis, our findings clearly confirm the hypothesis that valuable productive knowledge was generated through the FONTAR program, that this knowledge spilled over through labor mobility, and that knowledge diffusion is more intense in less concentrated industries.
The policy implications of our work are straightforward. First, our findings strongly support the most important justification of innovation policy, i.e. the incomplete appropriation of benefits by the investors in innovation. Therefore, the use of transfers – in the form or subsidies and matching grants – is certainly an advisable approach to promote knowledge creation and increase productivity. Second, because externalities in the form of spillover effects are often not precisely considered in ex-ante cost-benefit analyses of this kind of instrument, the decision on the magnitude of such interventions could be downward biased and lead to design programs that are not consistent with their potential social return, most likely undersized and underfunded.
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Table 1. Hypotheses
If B=0 the knowledge is too specific that has no market value.
Hypothesis 4 A>0, and B>0, C=0, D=0 if high concentration.
A>0, C>0, D>0 if low concentration (in this case D=0 is also possible if there is complete decay as in hypothesis 2)
Table 2. The mobility of workers in FONTAR firms Years in a FONTAR firm FONTAR firms Receiving firms Rest of firms
Number of employees 9,406 72 96 33,722 103 132 1,574,919 25 47
Table 4. Long-term effect of FONTAR on participant firms
Number of observations 805,495 805,495 34,563 805,495 805,495 805,495 805,495 Number of firms 126,080 126,080 6,150 126,080 126,080 126,080 126,080 province-year, multinational-year, type of society-year dummies, age and age squared, and a dummy variable that takes value one the year after the firm hired skilled workers and so thereafter. (c) Robust standard errors in parentheses. (d) ***, **, * statistically significant at 1%, 5%, and 10%.
Table 5. Effect on skilled workers who stay in FONTAR firm
Number of observations 9,570,703 9,570,703 9,570,703 Number of workers 1,523,211 1,523,211 1,523,211 Number of firms 123,438 123,438 123,438
R-squared 0.934 0.934 0.934 outcome variable, year, industry, province, multinational, and type of society dummies, firm’s age and age squared, worker’s sex, age, age squared, and tenure. (c) HHi is the Herfindahl-Hirschman index. (d) Clustered standard errors at the firm level in parentheses. (e) ***, **, * statistically significant at 1%, 5%, and 10%.
Table 6. Effect on knowledge carriers
Notes: (a) OLS estimates of lagged dependent variable model. (b) All regressions include three lags of the outcome variable, year, industry, province, multinational, and type of society dummies, firm’s age and age squared, worker’s sex, age, age squared, and tenure at the firm of origin. (c) HHi is the Herfindahl-Hirschman index. (d) Clustered standard errors at the firm level in parentheses. (e) ***, **, * statistically significant at 1%, 5%, and 10%.
Dependent variable: Wage (in logs)
Number of observations 2,628,556 2,628,556 2,628,556
Number of workers 358,626 358,626 358,626
Number of firms 174,413 174,413 174,413
R-squared 0.902 0.902 0.902
Ho: no serial correlation
(rho) 0.002 0.002 0.002
p-value 0.431 0.431 0.431
Table 7. Spillover effects
Number of observations 818,585 818,585 36,596 818,585 818,585 818,585 818,585 Number of firms 127,921 127,921 6,481 127,921 127,921 127,921 127,921
Notes: (a) OLS estimates of lagged dependent variable model. (b) All regressions include six lags of the outcome variable, year, industry-year, province-industry-year, multinational-industry-year, type of society-year dummies, age and age squared, and a dummy variable that takes value one the year after the firm hired skilled workers and so thereafter. (c) Robust standard errors in parentheses. (d) ***, **, * statistically significant at 1%, 5%, and 10%.
Table 8. Spillover effects by intensity of the knowledge diffusion Dependent variable:
Number of observations 818,585 818,585 36,596 818,585 818,585 818,585 818,585 Number of firms 127,921 127,921 6,481 127,921 127,921 127,921 127,921
R-squared 0.895 0.732 0.843 0.018 0.949 0.334 0.053
Ho: no serial correlation
(rho) 0.006 -0.002 0.000 - 0.002 0.004 -0.011
p-value 0.068 0.525 0.983 - 0.555 0.929 0.905
Notes: (a) OLS estimates of lagged dependent variable model. (b) All regressions include six lags of the outcome variable, year, industry-year, province-year, multinational-year, type of society-year dummies, age and age squared, and a dummy variable that takes value one the year after the firm hired skilled workers and so thereafter. (c) Robust standard errors in parentheses. (d) ***, **, * statistically significant at 1%, 5%, and 10%.
Table 9. The dynamics of the spillover effect
Observations 818,585 818,585 36,596 818,585 818,585 818,585 818,585
Number of firms 127,921 127,921 6,481 127,921 127,921 127,921 127,921
Notes: (a) OLS estimates of lagged dependent variable model. (b) All regressions include six lags of the outcome variable, year, industry-year, province-year, multinational-year, type of society-year dummies, age and age squared, and a dummy variable that takes value one the year after the firm hired skilled workers and so thereafter. (c) Robust standard errors in parentheses. (d) ***, **, * statistically significant at 1%, 5%, and 10%.
Table 10. The spillover effect by competition level
Observations 818,585 818,585 36,596 818,585 818,585 818,585 818,585
Number of firms 127,921 127,921 6,481 127,921 127,921 127,921 127,921 province-year, multinational-year, type of society-year dummies, age and age squared, and a dummy variable that takes value one the year after the firm hired skilled workers and so thereafter. (c) HHi is the Herfindahl-Hirschman index. (d) Robust standard errors in parentheses. (e) ***, **, * statistically significant at 1%, 5%, and 10%.
Figure 1: Dynamics of private sector employment. Average of monthly rates, 1997-2013
Source: OEDE.
3.5 44.5 55.5 6
1997 1999 2001 2003 2005 2007 2009 2011 2013 Year
Hirings Termination
Figure 2. Dynamics of the spillover effect on productivity term
Figure 3. Dynamics of the spillover effect on skill composition term