Productivity Spillovers Across Firms through Worker Mobility

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Feb 3, 2011 - Thus, poaching among Silicon Valley firms became so intense that Adobe Systems, Apple, Google, ... If there is learning by less productive firms from more ..... Table 2 gives a simple illustration of this shock. .... implying, as before, that receiving firms learn useful knowledge from more productive sending ...
Productivity Spillovers Across Firms through Worker Mobility Andrey Stoyanov and Nikolay Zubanov

February 3, 2011

Abstract We study productivity spillovers through worker mobility between Danish firms, by linking the receiving firm’s productivity one year after it hired new workers to the gap between the sending firms’ and own productivity one year before. Consistent with the spillover hypothesis, we find a positive effect of the gap on the receiving firm’s productivity when the gap is positive, and zero effect when the gap is negative. Surviving a variety of statistical controls, this effect increases with education and skill level of hired workers, persists for several years after the hiring was done, and remains broadly similar for different industries and measures of productivity. Explanations for this effect other than spillovers are discussed and ruled out. Key Words: Productivity spillovers, worker mobility, matched employer-employee data

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Introduction

Although knowledge spillovers, whereby firms improve their performance through learning from each other, have attracted considerable attention, the mechanisms behind them are little known. Earlier empirical studies proposed that patent citations facilitated spillovers (Jaffe, Trajtenberg, and Henderson, 1993; Hall, Jaffe, and Trajtenberg, 2001). This mechanism, however, cannot be the only one at work, or even the most important one, since patenting is practiced by relatively few innovating firms and excludes a wealth of uncodified knowledge, for example management practices. More recent studies focussed on employee turnover as an alternative mechanism (Song, Almeida, and Wu, 2003; Kim and Marschke, 2005). Besides being far more widely observed than patent citations, employee turnover is more likely to explain spillovers from large to small (more to less productive) firms, understanding which is important for the development and calibration of modern endogenous growth models with heterogeneous firms (Eeckhout and Jovanovic, 2002; Luttmer, 2007; Atkeson and Burstein, 2010). That new workers have been relied upon as a source of knowledge is evident as firms try to prevent their former employees from being hired elsewhere. For instance, many firms add non-compete covenants (NCCs) in their contracts with workers, resulting in a number of court cases dealing with their violations.1 Where NCCs cannot be enforced in court, as in California or North Dakota, firms sue their former employees for disclosing trade secrets, even though trade secret law violations are harder than NCCs to detect and prosecute. One prominent case is that of Hewlett-Packard (HP) versus its former CEO Mark Hurd, in which the plaintiff has sought injunction to prevent Hurd taking up employment with Oracle, HP’s competitor (The Economist, September 8th, 2010), on the grounds that he cannot do his work without necessarily revealing HP’s trade secrets. At the same time, one often observes firms poaching employees from competitors, to try to benefit from their knowledge. Thus, poaching among Silicon Valley firms became so intense that Adobe Systems, Apple, Google, Intel Corporation, Intuit, and Pixar agreed in 2009 not to approach each other’s employees, even at the risk of violating the U.S. competition law. The above examples suggest significant gains to be made from applying other firms’ knowledge brought in by their former employees. In this study, we attempt to estimate the value of this knowledge in terms of productivity gains by the receiving firms. (We therefore use the terms knowledge spillovers 1

Siegel, Brill, Greupner, Duffy & Foster (http://www.siegelbrill.com), a law firm based in Minnesota, lists fifty-six

non-compete cases heard in Minnesota courts alone during 2000-2008.

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and productivity spillovers interchangeably.) The first indication of productivity spillovers through employee turnover from our data is a positive correlation between the productivities of the sending and receiving firms. Denoting t the year in which a job changer left his old firm and joined a new firm, the correlation between the total factor productivity (TFP) in the new firm in t + 1 (one year after the move) and that in the old firm in t − 1 (one year before the move) is 0.15. Workers moving between similarly productive firms can hardly explain this correlation, since in year t − 1, when the leaver still worked in the old firm, the contemporaneous correlation between the old and new firms’ TFP was just 0.05. That this correlation is high when workers move from more to less productive firms (0.214), and low otherwise (0.097), further points to spillovers and suggests that less productive firms benefit from more productive ones, while the performance of the more productive firms is unaffected. Additionally, if job changers spread knowledge from more to less productive firms, one would expect a more concentrated productivity distribution in industries with higher rates of worker movement from more to less productive firms. In Figure 1 we plot the relationship between the TFP variance in twenty-one two-digit NACE industries and the 1995-2007 average turnover rate from top 40% to bottom 40% of firms (red dots) and from top quartile to bottom quartile (black dots). There is a strong negative correlation between worker turnover and TFP dispersion, −0.45 for the top to bottom 40% and −0.58 for the top to bottom quartile, which confirms our expectations. Moreover, that this correlation becomes stronger as we increase the productivity gap between sending and receiving firms suggests that productivity spillovers are a function of this gap – an insight we use for developing our empirical model in section 3. The remainder of this paper elaborates on the correlation between the receiving firm’s productivity in year t + 1 and the productivity gap between the sending and receiving firms in year t − 1. We add various controls to rule out explanations for this correlation other than productivity spillovers, and explore conditions under which it is expected to increase or decrease. One of such conditions has to do with heterogeneous effect of productivity gap. If there is learning by less productive firms from more productive, one would expect the correlation between the gap and productivity of the receiving firm to be positive when the gap is positive. At the same time, we would expect this correlation to be zero or insignificant when the gap is negative, since the knowledge brought in by workers from less productive firms cannot adversely affect the receiving firm. Both of these expectations are confirmed. Other conditions affecting the strength of productivity spillovers that we consider are common technology and education and skills of job movers. We propose that the knowledge transferred by job movers is

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to a large extent specific to a particular technology operating in a given industry, and find spillover effect within the same industry to be larger than when workers move between industries. We also find as expected that spillover effects are enhanced by education and skills of new hires, though even the least skilled workers are capable of bringing useful knowledge. In our analysis, we strengthen the case for productivity spillovers through employee turnover by ruling out alternative explanations to our statistical results. For instance, one might question the spillover interpretation of our findings, proposing instead that new workers’ human capital explains the correlation between the gap and the receiving firm’s productivity. It is true that workers recruited from more productive firms tend to have more human capital than the incumbent workers. However, human capital transfer falls short of explaining the asymmetry in the effect of the gap on the receiving firm’s productivity. Even then, we do allow for this alternative explanation to be borne out by the data, by controlling for observed and unobserved parts of the workers’ human capital through a measure developed in Abowd, Kramarz, and Margolis (1999), which is essentially the wage net of firm-specific effects estimated from the wage equation. Another alternative explanation to our findings relates to the difficulty of identifying spillover effects, since receiving firms may hire workers from more productive sending firms, whom they can now better afford, after experiencing a positive productivity shock in year t or t − 1. We present three approaches to eliminating this alternative. First, as part of our baseline empirical model, we control for the receiving firm’s productivity in t and t − 1; hence, we estimate average productivity effect of the gap for equally productive receiving firms. Second, as an extension to the baseline specification, we apply the estimator developed in Olley and Pakes (1996) which proxies productivity shocks by capital investments. Finally, as another extension, we repeat our analysis on the subsample of “green field” firms which did not exist in t − 1, and thus no productivity shock could have affected their hiring choices. All approaches bring similar results to our baseline specification, suggesting that firms’ hiring practices are little affected by past productivity shocks. Our three most important findings are as follows. First, we find that, at the hiring rate of 10% of its workforce (about sample average), the receiving firm gains 3.5% of the positive productivity gap and about zero of the negative gap. For an average firm, this implies a productivity gain from spillovers of 0.84% a year. The asymmetry of the gap’s effect implies that productivity gains from spillovers will accrue only when new workers come from firms having more knowledge factored in their productivity. Second, the gap’s effect lasts at least five years, implying the long-run spillover effect from hiring even 4

larger than 0.84%. The persistence of the productivity gap’s effect suggests long-term consequences of spillovers for receiving firms, and is also a novel finding. Finally, spillovers occur through hiring even the least skilled workers, which implies that a substantial part of the knowledge transferred by job movers is not particularly sophisticated and thus unlikely to be patented or otherwise codified. Taking account of this knowledge, as we do in this this study, is therefore an important addition to the R&D spillovers literature typically dealing with codified knowledge embodied in patents. Our work’s contribution to the literature on spillovers goes beyond factual findings. Thus, our unique data enable us to present what we believe is most detailed and robust empirical evidence to date on the source of spillovers through employee turnover, and their size. We find a direct spillover effect on the receiving firm’s productivity rather than on some indirect indicator of firm performance such as the number of patents or R&D expenditure. Therefore, our results can be directly applied in calibrating theoretical models of spillovers to analyze the effect of labor mobility on the distribution of firm size, productivity, growth, and welfare. We identify a measure of the receiving firm’s exposure to spillovers – the productivity gap – thus extending the relevant literature which has so far operated with a 0 − 1 variable denoting experience at a foreign firm, or an aggregate thereof. The gap performs well in our regressions, giving consistent results for various measures of it: value added, TFP, and profit. Furthermore, our research widens the scope of the literature on spillovers through labor mobility, by studying the spillovers of uncodified as well as codified knowledge happening between nearly all firms in the Danish manufacturing sector. Our results are thus more widely applicable, while agreeing with those reported in earlier studies. The rest of the paper is organized as follows. In section 2, we take stock of the existing literature on spillovers and identify directions in which we can contribute to it. The empirical model underlying our study is outlined in section 3; therein we also discuss how we deal with the estimation issues involved in identifying spillovers through employee turnover. Section 4 describes our data. The regression results along with various extensions and robustness checks are presented in sections 5, 6 and 7. Section 8 concludes.

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Literature review

It is commonly agreed that not all knowledge which benefits firm economic performance is generated within a firm, but a large portion of it is acquired, or ‘spilled over’, from other firms. In the theoretical 5

literature, knowledge spillovers are often described as a major channel for economic growth (Romer, 1990, Grossman and Helpman, 1991). In particular, spillovers from old (large, more productive) to new (small, less productive) firms is a central element of many recent models of endogenous growth with heterogeneous firms (Eeckhout and Jovanovic, 2002; Atkeson and Burstein, 2010; Luttmer, 2007). For instance, Luttmer (2007) relies on spillovers to “ensure that the technologies available to potential entrants are never so far behind those of incumbent firms that entry of new firms is not feasible” (p.1106). The empirical literature on spillovers has developed in two directions. Studies within the first direction found productivity spillovers within particular industries or geographical areas. Early work on technological innovations (see Griliches, 1992, for a survey) showed that firms’ productivity depends positively on both its own R&D spending and on that of other firms in the same industry or geographic area. More recent studies revealed that spillovers are not confined to R&D-intensive firms and industries. Thus, the findings of Battu, Belfield, and Sloane (2003) and Martins and Jin (2010) that worker wages grow with the average workforce education level in a firm, and of Moretti (2004) that firms’ productivity increases with their region’s workforce average education level, indicate that spillovers are in fact ubiquitous. Another direction in the spillovers literature, to which our study also belongs, has looked into mechanisms behind spillovers. Although historically the first spillovers mechanism proposed was patent citations (Jaffe, Trajtenberg, and Henderson, 1993; Hall, Jaffe, and Trajtenberg, 2001), it is employee turnover that fits the ubiquity of spillovers better. Maintaining a historic focus on innovation activities, a number of studies have looked at R&D spillovers through turnover, and generally found that hiring workers from firms actively engaged in R&D increases the same activity in their new firms. Thus, Rao and Drazin (2002) establish that new firms with low rates of product innovation tend to recruit workers from more established rival firms, which stimulates their own innovation activity. Song, Almeida, and Wu (2003), looking at the patenting activities of firms in the U.S. semiconductor industry, find that higher mobility of engineers within the sector is associated with increased citations by their new employers of the patents of their previous employers. Kim and Marschke (2005) show with a theoretical model and empirically that the risk of R&D workers moving to other firms reduces R&D expenditure by their current employer and stimulates it to patent more in order to protect itself against the possibility of knowledge duplication by the departing worker. Kaiser, Kongsted, and Thomas (2008) show that R&D workers (defined as those with a university degree in science) coming from

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firms with active patenting tend to increase patenting activity in their new firms. Maliranta, Mohnen, and Rouvinen (2009) using Finnish matched employer-employee data, show that labor productivity, profitability and average wages are all positively affected by hiring workers involved in R&D activities at their previous firms. Theirs is one of the very few studies in this domain looking not at inputs, such as R&D, but at performance outcomes as we do. One common limitation of these studies is that only relatively few well-established firms are actively engaged in R&D; hence, it is difficult to study productivity spillovers from large (more productive) to small (less productive) firms by looking at the exchange of workers between such firms. Another limitation is that, relying on R&D activities such as patenting as a measure of knowledge being transferred, one will miss spillovers of uncodified knowledge which can also affect firm performance. In fact, uncodified knowledge is likely to be more important economy-wide than its codified variety.2 A more inclusive performance measure and a wider data sample are required to address these limitations. The literature on spillovers from foreign to domestic firms has made good progress addressing the above limitations. It is motivated by foreign-owned firms being more productive than domestic firms and possessing superior technologies and business practices, a significant portion of which is embedded in individuals and is thus transferable. Our approach echoes this literature in that we too focus on spillovers from more to less productive firms. Notable studies in this field are Gorg and Strobl (2005) who find that domestic businesses in Ghana managed by ex-employees of foreignowned companies tend to be more productive and less likely to exit the market, and Malchow-Moller, Markusen, and Schjerning (2007) who develop a Melitz-type model of trade with heterogeneous firms where ex-employees of (more productive) foreign firms transfer their knowledge to their new domestic employers. Applying their model to Danish data, they show that workers with an experience in foreignowned firms enjoy a wage premium at domestic firms in return for this knowledge. Relatedly, Poole (2009) finds a positive effect on wages paid in domestic firms in Brazil of the share of new workers with an experience at foreign-owned firms. She identifies this effect as evidence of knowledge transfer from foreign firms, since hiring workers with similar human capital but not previously employed at foreign firms had no effect on wages. As Poole (2009), we also use a measure of human capital in the spirit of Abowd, Kramarz, and Margolis (1999) to isolate spillover effects from those due to the new workers’ human capital. (More detail on our implementation of this measure, and on our identification 2

For example, according to Duguet and MacGarvie (2005), 36% of French firms said they had obtained new technolo-

gies by hiring new employees, whereas only 17% learned from patents and licenses.

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strategy in general, in the following section 3.) Though closest to our research, the literature on foreign–domestic spillovers has important limitations as well. First, it uses a 0 − 1 variable denoting a new worker’s experience at a foreign firm (or an aggregate thereof at the firm level), thus losing potentially useful variation of the amount of knowledge to be learned through hiring ex-foreign firm employees. Second, and relatedly, it concentrates on foreign-owned companies as the source of spillovers, whereas domestic firms can produce spillovers as well. We address both these limitations by looking at all firms in the economy, and by introducing a measure of spillover as we explain in the next sections.

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Empirical model

We estimate spillover effects from the relationship between the productivity level of the receiving firm and the gap between the productivity of the sending and the receiving firms, including lags of receiving firm’s productivity and additional controls (vector Y) which we will specify later. Thus, the initial model looks as follows: Ari,t+1 = α1 Ari,t + α2 Ari,t−1 + β · gapi,t−1 · Ii,t + Yi,t γ + εi,t+1 ,

(1)

where indices i, and t denote worker and year, respectively, Ar refers to a measure of the receiving firm’s productivity, Ii,t is an indicator variable equal to 1 if worker i changes firm in year t and 0 otherwise, gapi,t−1 = Asi,t−1 − Ari,t−1 is the productivity gap between worker i’s previous and current employers in year t − 13 , and εi,t+1 is the error term. Thus, if worker i did not change firms in year t, equation (1) is just an AR(2) model; the extra term, gapi,t−1 , is added if she did. The task is to estimate coefficient β in equation (1), which measures the average effect of productivity gap on the receiving firm’s productivity. However, the problem with estimating (1) is that its dependent variable, Ari,t+1 is the same for all workers working in a given firm in a given year, while part of the gap variable, Asi,t−1 , varies by worker. Ari,t+1 staying fixed within the same firm induces a negative correlation between Asi,t−1 and the error term, because, all other regressors being fixed in a 3

Since we only have records of the worker’s employer at the end of the calendar year, the exact time of job transfer is

unknown, so we analyze the relationship between productivity of sending firm in year (t − 1) and that of the receiving firm in year (t + 1) .

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given firm and year, the error term and Asi,t−1 will be moving in opposite directions. Consequently, the least squares estimate of β from (1) will be downward-biased.4 Still, β can be consistently estimated from a direct analogue of equation (1) specified at the firm level (j indexes firms): ¯ 1 γ2 + Y ¯ 2 γ3 + ε¯j,t+1 , g j,t−1 + Xj,t γ1 + Y Arj,t+1 = α1 Arj,t + α2 Arj,t−1 + β · gap j,t j,t

(2)

PNj,t

Ii,t (Asi,t−1 − Arj,t−1 )/Nj,t , Nj,t is the total number of workers in firm j in ¯ 1 = PNj,t (1 − Ii,t ) Yi,t /Nj,t is a set of firm-average year t, Xj,t is the set of firm characteristics, Y j,t i=1 PNj,t 2 ¯ characteristics of incumbent workers at period t, Yj,t = i=1 (Ii,t Yi,t ) /Nj,t is a set of firm-average g j,t−1 = where gap

i=1

characteristics of workers hired at year t, and Xj,t are firm-level characteristics. Note that the expresP j,t s Hj,t r g j,t−1 = H g j,t−1 in equation (2) can be rewritten as gap sion for gap i=1 (Ai,t−1 − Aj,t−1 )/Hj,t · Nj,t , where g j,t−1 is the average productivity Hj,t is the number of new workers hired by firm j in year t. That is, gap gap weighed by the share of new workers. Intuitively, weighing by the share of new workers is needed to account for the exposure of the receiving firm to new knowledge as measured by the sending firms’ productivity: the larger the share, the higher the exposure.

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g j,t−1 is free To take β as evidence for spillover effects from hiring, we must ensure that: (i) gap from the effect of new workers’ human capital, and (ii) it does not pick up unobserved shocks to the receiving firm’s productivity coinciding with hiring new workers. The first requirement is important because a sending firm’s productivity and its workers’ human capital are likely to be correlated. Not isolating this correlation could therefore confuse spillovers with human capital transfer, the two effects having different nature and implications. To control for this correlation, we introduce a comprehensive measure of workers’ human capital, including a variety of observed characteristics (age, gender, salary, experience, education, professional status), as well as unobserved human capital. Our measure of unobserved human capital, based on the work of Abowd, Kramarz, and Margolis (1999), uses workers 4

Consider a regression where the dependent variable is the same for all observations i from group j but the independent

variables vary by i and j: yj = xi,j β + ε (i). If the researcher is interested in the effect of the group average x ¯j = PNj i=1 xi,j /Nj on y, estimating equation (i) will produce the wrong estimate, since the OLS estimate for β from this equation is β˜ = (x0 x)−1 x0 y = (x0 x)−1 x ¯0 y, whereas the correct estimate is βˆ = (¯ x0 x ¯)−1 x ¯0 y. 5 Estimating equation (2) with the average gap not weighed by the share of new workers gives a lot less precisely estimated regression coefficients (though still statistically significant), presumably because, unless controlled, the variation in the degree of exposure to new knowledge among the receiving firms becomes part of the measurement error in the gap variable.

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movement across firms to identify the person-specific component θi from the wage equation: wi,j,t = λ + xi,j,t β + θi + ψj + εi,j,t

(3)

Specifically, our measure of workers’ human capital in the receiving firm includes both observed and unobserved components and is calculated as the average of individual measures, hj,t = xi,j,t β + θi + εi,j,t = 1 PNj,t i=1 (wi,j,t − λ − ψl ). Note that hj,t is free from firm-specific effects on wage (such as compenNj,t sation policies or aggregate productivity), ψ, and hence is a good proxy for portable, rather than firm-specific, human capital. The second requirement concerning the validity of our procedure of estimating spillovers concerns endogeneity of the productivity gap in equation (2). Namely, if a receiving firm j experiences a positive productivity shock in year (t − 1) it may respond by hiring workers from more productive firms who are likely to be of better quality and whom it can now better afford. Then, in addition to g j,t−1 will carry firm j’s own productivity shock the effect of sending firms’ average productivity, gap in t − 1. We present three approaches to isolating this productivity shock. First, as part of our baseline empirical model, we control for the receiving firm’s productivity in t and t − 1; hence, we estimate average productivity effect of the gap for equally productive receiving firms. Second, as an extension to the baseline specification, we apply the estimator developed in Olley and Pakes (1996) which proxies productivity shocks by capital investments (results presented in section 7.2). Lastly, as another extension, we repeat our analysis on the subsample of “green field” firms which did not exist in t − 1, and thus no productivity shock could have affected their hiring choices. The results for new firms are reported in section 6.4. All three approaches produce similar results, implying that the productivity gap’s effect on which we base our main story cannot be explained by unobserved productivity shocks.

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Data

The key features of our data, provided by Statistics Denmark, are nearly total coverage of employees and firms, and the match between the employee and firm records. Both these features makes these data particularly suitable for our purposes. We use manufacturing firm-level data from 1995 to 2007 which include sales, employment, value added, materials and energy input, profit, fixed assets stock and investments, and the two-digit NACE industry identifier. A large part of firm-level data comes 10

from annual surveys in which all firms employing fifty or more workers must participate. For smaller firms, the data are interpolated and therefore are less reliable. The individual-level data are available from 1983 onwards and cover all individuals aged fifteen to sixty-five and include salary (if applicable), age, gender, experience in thousands of hours, highest completed education, and occupation. In the analysis that follows, we only include manufacturing employees with a positive annual salary. All individuals with multiple jobs are treated as different. Educational attainment is measured in three levels: high school, college, and university. The occupation variable consists of three categories: lowskilled and skilled workers, and managers. The employment record is as of the end of the calendar year, so if a worker changed job we only observe the year in which it happened. The dependent variable in most of our analysis is firm’s productivity measured as value added per worker weighted by firm’s average wage relative to the industry average. This weighing is applied to control for differences in productivities which originate from differences in labor quality across firms and are reflected in workers average compensation. In addition to applying weights to our main productivity measure, we also include various measures of workers’ human capital in regression specifications as extra controls for human capital effects. Furthermore, in Section 7.5 we try several alternative measures of productivity (to which weighing is not applied), and obtain similar results. Table 1 lists descriptive statistics measured at the worker level. Between 1995 and 2007 there are about 5.8 million worker-year observations, of which 668,034 are job changers, implying an average hiring rate of 11.5%. The average job stayer is 39.6 years of age and has 14.7 years of experience. The majority of stayers have a (technical) college degree (49.8%), 12.2% have a university degree, and 38% a high school diploma. Most are classified as skilled (65.4%) followed by less skilled (25.5%) and managers (9.2%). In comparison, the average job changer is 2.7 years younger and has half a year less experience. He is more likely than a job stayer to be a skilled worker (71.8% vs. 65.4%) and to have completed an education beyond high school (64.3% vs. 62%). There is no difference in the average productivity of the job changer’s previous and new firm: both produced about 580,000 Krones worth (= e6.36 ) of value added per worker per year. These averages, however, disguise a significant variation in the productivity levels of the sending firms from which new workers are hired. Thus, for an average receiving firm, 45% of new hires come from more productive firms (10% above market average) and 55% from less productive firms (9% below market average). During the time period covered by our sample the wage of an average job stayer was 205,000 Danish Krones (= e12.23 ), or 27,000 Euro, per annum. The salary of an average job changer was 2% 11

below that, but those moving from more to less productive firms, as measured by value added per worker, earned, on average, 3% extra. This wage premium is consistent with the idea that firms try to attract workers from more productive firms by offering them higher salaries. Having said this, other reasons such as higher quality of workers coming from more productive firms, as well as new employers’ characteristics, most notably larger firm size and per-workers value added, cannot be ruled out at this point. Table 2 shows summary statistics aggregated at the firm level. Productivity and wages averaged at the firm level are lower than their respective averages at the worker level, as a result of smaller firms, which weight in total observations is now greater, being less productive and paying less than larger firms. Of the 173,929 firm-year observations in the sample, hiring took place in about half (85,123). Size is the biggest difference between hiring and non-hiring firms: the hiring firms tend to be larger. Another important difference is that hiring firms are more productive. As we wrote in section 3, the productivity shock that might have caused the expansion of the hiring firms needs to be isolated for the proper identification of spillovers. Table 2 gives a simple illustration of this shock.

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Results

5.1

Baseline results

Table 3 reports the benchmark regression results for equation (2) estimated for all manufacturing firms during 1995-2007. The dependent variable is firm productivity measured as value added per worker weighted by firm’s average wage relative to the industry average (see section 4). Each specification includes as controls the AR(2) lags of the receiving firm’s productivity, the average size of sending firms, and year dummies. The estimates are consistent with our hypothesis of productivity spillovers through worker turnover. For instance, the coefficient 0.199 in column (1) (the specification with no additional controls) implies that, at the hiring rate of 10% of the total yearly headcount (close to the sample average hiring rate of 10.6% per firm), the productivity gain of the receiving firm is 1.91% of the average gap. In the next three specifications, we include firm characteristics (column 2) followed by averages of incumbent (column 3) and newly hired workers’ (column 4) characteristics.6 As a result, the estimate 6

The coefficient estimates for the control variables are skipped for brevity but are available on request.

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for the effect of the gap goes down a little, to just under 0.18. One of the most important controls here is the newly hired workers’ human capital measured as their wage net of firm-specific effects (see section 3) and averaged at the firm level. One could argue that, because workers at more productive firms tend to be more productive themselves, part of the positive effect of productivity gap is due to the new workers’ human capital rather than knowledge transfer. We do indeed find that more human capital of the new hires, as well as incumbent workers, is associated with higher productivity in the receiving firms. Yet, since the inclusion of human capital is largely inconsequential for the estimate of our interest, our conclusion is that this estimate measures the effect of knowledge transfer rather than that of human capital. Telling between these two effects is important: while human capital, in its portable part that we measure, is adequately priced on a competitive labor market, the transfer of knowledge from one company to another is an externality which may require a policy response.

5.2

Spillovers from more and less productive firms

Our baseline specification (2) implies that the productivity effects are equal whether a worker is hired from a more or a less productive firm. In other words, if a worker is hired from a 10% less productive firm, the effect of the receiving firm’s productivity will be negative, and if another worker is hired from a 10% more productive firm, the total effect will be zero. While it is possible, in principle, that positive effects of new knowledge obtained from more productive firms will be offset by the costs of training up workers from less productive firms, our human capital measures should account for the latter effect. Hence, since there can be no negative learning, the effect of the gap should be positive when the gap itself is positive, and zero when the gap is negative. Consequently, in Table 4 we present the results for specification (4) where the averages are calculated separately for positive and negative productivity gaps: g Pj,t−1 + βN · gap gN Arj,t+1 = α1 Arj,t + α2 Arj,t−1 + βP · gap j,t−1 ¯ 1 γ2 + Y ¯ 2P γ3P + Y ¯ 2N γ3N + ε¯j,t+1 , +Xj,t γ1 + Y j,t j,t j,t P

g j,t−1 = where gap

PNj,t i=1

N

g j,t−1 = Ii,t Di,t (Asi,t−1 −Ari,t−1 )/Nj,t , gap

PNj,t i=1

(4)

Ii,t (1 − Di,t ) (Asi,t−1 −Ari,t−1 )/Nj,t ,

and Di,t is an indicator variable equal to one if (Asi,t−1 −Ari,t−1 ) > 0.7 Note that in this specification we control for new worker characteristics separately for those hired from more and less productive firms. 7

2P 2N ¯ j,t ¯ j,t Variables Y and Y are defined similarly.

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The results in column (1) confirm our expectations. The estimate for the negative productivity gap is insignificant, implying that there are no gains from hiring workers from less productive firms, but no losses either. The effect of hiring workers from more productive firms is 0.351, almost twice as high as that estimated for the total gap (0.179). These two estimates are consistent with each other: since 45% (55%) of all job changers move from more (less) productive firms, the positive productivity effect of hiring a worker from a more productive firm is mixed with the neutral effect of hiring a worker from a less productive firm. As an extra extension to the results for the positive and negative productivity gaps separately, we include their squares (column 2), to see whether the knowledge spillover effect is getting stronger or weaker as the gap between sending and receiving firms widens. We find that there is a second-order effect of the positive productivity gap, implying that gains from new knowledge increase somewhat faster than the productivity gap. However, because this effect is only weakly significant and of small magnitude, we will not include it in further extensions presented in the following section. The main message from the results in Table 4 is that the effect we capture is not due to the sending firm workers’ human capital, since it would have been symmetric otherwise. Another important message is that hiring is beneficial as long as at least some new workers come from more productive firms, even if the average productivity gap across all new hires is negative. To illustrate, with the P mean value of gg ap equal to 2.4%, the estimate of βP = 0.35 implies that the effect of knowledge

transfer through labor turnover on productivity of an average receiving firm is 0.35 × 2.4% = 0.84 percentage points a year. In other words, a median firm would move to 51st centile if it were the only one to hire workers from more productive firms.

6 6.1

Extensions Spillovers within and between industry sectors

Knowledge can be general or specific to a particular firm or industry. Firm-specific knowledge does not get transferred, and so cannot be part of the effect of the productivity gap. General knowledge, while perfectly transferable, can be learned through many channels and does not require hiring workers from more productive firms. Previous results suggest that knowledge coming with new hires is general enough to be applied in different firms. In this section, we want to see whether, and to what extent,

14

this knowledge travels through technologies employed in different industries. Accordingly, we estimate the effect of the productivity gap separately for workers hired from firms within the same two-digit industry (NACE classification) and for those hired from outside, with the following equation: f g same g dif Arj,t+1 = α1 Arj,t + α2 Arj,t−1 + βdif f · gap j,t−1 + βsame · gapj,t−1 +

¯ 1 γ2 + Y ¯ 2 γ3 + ε¯j,t+1 Xj,t γ1 + Y j,t j,t g same where gap j,t−1 =

PNj,t i=1

same (As r g dif f Ii,t i,t−1 − Ai,t−1 )/Nj,t , gapj,t−1 =

PNj,t

i=1 (1

(5)

same )(As r − Ii,t i,t−1 − Ai,t−1 )/Nj,t ,

same is an indicator variable which takes the value of one if worker i moved from one firm and Ii,t

g same to another in period t within the same two-digit industry, and zero otherwise. That is, gap j,t−1 and f g dif gap j,t−1 are productivity gaps between the same and different industry sending firms and the receiving

¯ 2 are also firms weighed by their respective shares in the receiving firm’s workforce. Variables in Y redefined separately for the workers coming from within and outside the industry where the receiving firm belongs. There are nine two-digit industries in the manufacturing sector, and 55% of all job changes took place between firms operating in the same industry. If knowledge transferred by new hires knows no technology borders, the coefficients β same and β dif f should be equal. In fact, as Table 5 shows, the gap’s effect is much stronger when workers come from the same industry. Breaking down both productivity gaps onto their positive and negative parts, in the same way as in equation (4) (column 2), we find that only positive productivity gaps are significant, implying, as before, that receiving firms learn useful knowledge from more productive sending firms, while hiring workers from less productive firms brings no extra benefit or cost apart from their human capital. That the effect of hiring workers from more productive firms within the same sector (0.33) is about 60% larger than for workers from other sectors (0.21) implies that knowledge brought in by new workers is in large part industry-specific. Hiring within the same industry thus brings more relevant new knowledge than what can be learned from workers previously employed outside.

6.2

Spillovers by worker education and occupation

Previous research found differences in the ability of workers to transfer and apply new knowledge depending on their occupation (Song, Almeida and Wu, 2003) and education (Kaiser, Kongsted and Ronde, 2008). In this section, we apply insights from these studies to ascertain whether new hires’ education and occupation within their sending firms influence the strength of spillovers. Starting with

15

education, we classify the new workers’ educational attainment into three categories: high school, college (or a comparable vocational degree), and university degree. We upgrade equation (2) by including interactions between gap and education level dummies as follows: g coll g high Arj,t+1 = α1 Arj,t + α2 Arj,t−1 + βhigh · gap j,t−1 + βcoll · gapj,t−1 + univ ¯ 1 γ2 + Y ¯ 2 γ3 + ε¯j,t+1 βuniv · gg apj,t−1 + Xj,t γ1 + Y j,t j,t

g kj,t−1 = where gap

PNj,t i=1

(6)

k (As r k Ii,t i,t−1 − Ai,t−1 )/Nj,t , k = {high school; college; university}, Ii,t is an

indicator variables which takes the value of one if worker i with education level k was hired by firm j k

apj,t−1 is the average productivity of firms from where workers with education in period t. Therefore, gg ¯2 level k were hired, weighed by their respective shares in total workforce. We also include in vector Y firm average characteristics of new hires by level of education weighed by their shares in the receiving firm’s workforce. If more educated workers can convey knowledge better, then we would expect βhigh < βcoll < βuniv , meaning that higher educated workers will transfer more knowledge and hence will cover a higher share of the productivity gap between their old and new employers. Table 6 presents estimation results for equation (6). The effect of the productivity gap is stronger for new workers with college or university degrees. For instance, for college graduates this effect is 75% greater than for high school graduates, and the effect for university graduates is more than 2.5 times as large. Even though the latter effect is estimated with high variance and is not significant individually, there is a clear tendency for the spillover effects to increase with the level of education of new workers. Breaking the productivity gaps by education level down to positive and negative parts (column 2) shows the same tendency for the gap’s effect to grow with education and produces larger estimates for the positive part of the gap. Thus, hiring 10% of the workforce from a 10% more productive firm will raise productivity of the receiving firm by 0.54% if the new hires have university degrees, and by about 0.28% if they have a college degree or a high school diploma. Workers coming from less productive firms bring no new knowledge regardless of their education level. Turning to differences in the efficiency of knowledge transfer by worker occupation in the sending firm, we divide all workers into three categories: unskilled, skilled, and managers, and estimate the following version of equation (2): unskilled

g j,t−1 Arj,t+1 = α1 Arj,t + α2 Arj,t−1 + βunskilled · gap manager

g j,t−1 βmanager · gap k apj,t−1 = where gg

PNj,t i=1

skilled

g j,t−1 + + βskilled · gap

¯ 1 γ2 + Y ¯ 2 γ3 + ε¯j,t+1 + Xj,t γ1 + Y j,t j,t

(7)

k (As r Ii,t i,t−1 − Ai,t−1 )/Nj,t , k = {unskilled; skilled; manager} indicates worker’s

16

k is an indicator variables which takes the value of one if worker i’s occupation at the occupation, Ii,t

new firm is k. Table 7 reports estimation results. At the first glance (column 1), unskilled workers appear to be able to transfer knowledge just as well as skilled workers, and only managers are more efficient at it than the rest. However, when we look at the effects of occupation in knowledge transfer separately for more and less productive sending firms (column 2), the estimates follow the same pattern as for education. If coming from more productive firms, skilled workers can transfer knowledge 50% more efficiently than unskilled workers, and managers are twice more efficient.8 As before, the effect of new workers coming from less productive firms is statistically insignificant for all occupations. In sum, the findings in this section demonstrate that productivity spillovers through labor mobility are larger when new hires are more educated and more skilled as defined by their occupation, since they had more opportunity to accumulate knowledge in their previous firms and apply it with their new employers. These workers should therefore be more attractive to potential employers. However, that even unskilled workers can transfer some valuable knowledge suggests that knowledge transferred through hiring does not have to be of a particularly sophisticated nature and can consist, in part, of relatively simple techniques and tricks. Since this knowledge is unlikely to be patented or otherwise codified, labor turnover seems the most plausible and reliable mechanism of its transfer.

6.3

Spillover dynamics

As another extension to our baseline results, we study the persistence and duration of the productivity gap’s effect on the receiving firm’s productivity. Estimates of specification (4) imply that hiring workers from more productive firms today will raise productivity in the next year. Since firm’s productivity is an autoregressive process, the effect of a one-off hiring will carry over a number of years through the AR(2) terms and can be estimated from the impulse response function. For instance, given the estimates for the AR(2) terms in equation (4), the implied impulse response estimates for the effect of the positive productivity gap in year t − 1 on the productivity in years t + 2 to t + 5 are 0.168, 0.176, 0.130 and 0.110, respectively. However, this effect does not have to propagate exclusively through autoregression and can have its own dynamics. In particular, if the gap’s effect is more persistent 8

As with the effect of university degree on spillovers, the coefficient βmanager has high variance and the effect is

therefore insignificant. The likely reason here is the relatively small number of managers among the job changers. Thus, g manager was zero for 98% of observations. the variable gap

17

than the AR(2) process for productivity in (4), the impulse response function will underestimate the dynamics of productivity spillovers through hiring. To gauge the persistency of the effect of productivity gap in period t − 1 on the receiving firm’s productivity in periods t + 2 onwards, we estimate the following forecast equation Arj,t+k

=

α1k Arj,t

+ α2k Arj,t−1

+

k−2 X

¯ 1 γk + Y ¯ 2 γ k + ε¯j,t+k , k > 1 (8) g j,t−1 + Xj,t γ1k + Y δi Asj,t+i + β k gap j,t 2 j,t 3

i=0

This method, also known as local projections (Jord`a, 2005), is easy to implement and more robust to possible dynamic misspecifications in the original regression equation that estimating from impulse responses. Because the AR(2) terms in equation (8), Arj,t+k−1 and Arj,t+k− , are substituted recursively for Arj,t and Arj,t−1 , the coefficient β k measures the total effect of the productivity gaps in t − 1 on the productivity in t + k, both through autoregression and own dynamics. Specification (8) also includes P  k−2 s all leads of sending firms’ productivities i=0 δi Aj,t+i to control for the regular spillover effects on the receiving firm’s productivity in year t + k of sending firms’ productivity two year earlier. The results are presented in Table 8. Moving the dependent variable forward in time in specification (8) reduces the sample size due to firms’ exit. Therefore, for consistency, we estimate the dynamics of productivity spillovers using only the subsample of firms with observations available for at least five consecutive years. In the first two columns of Table 8, the estimated spillover effect is higher than on the entire sample (Table 4), for instance, the positive gap’s effect is 0.536 compared to 0.351 in Table 4. However, the subsample in Table 8 is also different from the entire sample in that it consists of relatively long-lived firms which are bigger and more productive than average. As a result, the average positive gap on this subsample is smaller, and the mean spillover effect is of about the same magnitude as on the entire sample. The results in Table 8 also show that much of the gap’s effect preserves for as long as six years after a hiring is done. Consistent with our previous findings, this lasting effect rests on hiring workers from more productive firms. For instance, the effect of hiring workers from more productive firms in year t on productivity in years t + 2 to t + 5 is two to three times stronger than that implied by the impulse response function alone. Hence, the knowledge brought in by new workers brings their new firms sustained competitive advantage rather than a short-term productivity boost. Another, more technical, implication of the persistence of the gap’s effect is that a large part of the spillover effect applies to the receiving firm’s average productivity. Estimating this effect in isolation from other 18

firm-specific characteristics contributing to its average productivity is, therefore, difficult. Indeed, as we find in section 7.3, the fixed effects estimator, using only the within-firm variation of gap in time, gives a much lower estimate.

6.4

Spillover effects for new firms

In this section we look at the productivity spillover effect for startups, which will extend our results in two ways. First, as was discussed in the introduction, knowledge spillovers through labor mobility can present a valuable opportunity for small and less productive firms to learn from more successful competitors. Focusing on the startups is thus an ideal setting to study this effect, since new entrants are typically small and less productive and have a lot to learn from more experienced firms. Second, as discussed in the empirical model section 3, past productivity shocks can affect firm’s hiring strategy and thus stimulate firms to seek new workers with characteristics more likely to be observed among the personnel of more productive firms. For startup firms there is no performance history, and hence no feedback from past events to current hiring practices. To estimate knowledge transfer effect for startups, we use the following reduced form of model (2): fs ¯2 Arj,t+1 = α1 Arj,t + β · A ¯j,t+1 , j,t−1 + Xj,t γ1 + Yj,t γ3 + ε

(9)

¯ 1 variables are removed due to their absence for startups. Similarly, the where the Arj,t−1 and Y j,t productivity gap cannot be constructed for startup firms and the relationship we explore is the one PNj,t es s between average productivity of sending firms, A j,t−1 = i=1 Ii,t Ai,t−1 /Nj,t weighed by the share of new workers, and future productivity of the receiving firm, Arj,t+1 . The coefficient β in equation (9) is nevertheless comparable to the β in (2), since (9) is its reduced form. Estimation results for equation (9) are presented in column (1) of Table 9. Positive and significant coefficient β corroborates our earlier findings on productivity spillovers for the entire sample of firms. At the hiring rate of 100% for startups, a 10% increase in the average productivity of the sending firms leads to a 2.06% increase in the productivity of the receiving firm - an estimate close to that for the whole sample of receiving firms with all controls included (Table 3). Breaking the sending firms down to above- and below the manufacturing sector’s average productivity (column 2), we observe the same tendency as before: the productivity effects of hiring from more-than-average productive

19

firms are stronger than from less productive.9 Although the gap’s effect is also present for less-thanaverage productive sending firms, this effect is much smaller than the effect brought in by workers from above-average productive sending firms. Finally, in columns (3-4) we report estimates of the effect of sending firms’ average productivity on the probability of the receiving firm’s survival in the first year after its start. Consistently with its effect on productivity, higher average productivity of sending firms increases the chances of the receiving firm’s survival (column 3), but this effect seems to work only for hires from firms with above average productivity (column 4). To gauge the importance of productivity spillovers for a startup’s survival, we calculate the marginal effect of the sending firms’ productivity for an average receiving firm (shown in square brackets in Table 9). The estimated marginal effect of hiring from firms with above average productivity (column 4 in Table 9) implies that a 10% increase in sending firms’ productivity results in 0.26% increase in the receiving firm’s probability of survival, a very small increase relative to the first year exit probability of 16.7%.

7

Robustness tests

7.1

Contemporaneous correlation between sending and receiving firms’ productivities

In every regression specification so far we have maintained that productivity of the sending firm in year t − 1 (when the worker in question was still employed there) will have an effect on productivity of the receiving firm starting from year t + 1, the year after the worker changed firms. Indeed, it takes time for the new employee to transfer his knowledge to new coworkers, to convince the management to change their practices, and to observe the effect of these changes on the receiving firm’s performance. Suppose, however, that there is a productivity effect instantaneous to hiring decision in year t. Such an effect is unlikely to be due to spillovers, and its existence would question our interpretation of the gap’s effect as evidence of spillovers and suggest instead that there may be other factors co-determining the sending and receiving firms’ productivity in each year. Adding a contemporaneous measure of sending firms’ productivity would control for these factors and reveal the independent effect of productivity gap, bringing us closer to a causal interpretation of our earlier results. 9

fs , reveals the same pattern. An alternative specification, one with the square and cubic terms for A

20

In columns (1)-(2) of Table 10 we report estimation results from the specification (2) augmented with the average productivity of sending firms from which workers were recruited in year t + 1 (Asi,t+1 ). The estimates show that recruiting 10% of total workforce from a 10% more productive firms is associated with an 0.076% instantaneous increase in productivity of the receiving firm, which result presumably reflects the positive correlation between productivity levels of sending and receiving firms reported in the introduction. The contemporaneous link between sending and receiving firms’ productivity is weak relative to the effects of productivity gap in years t − 1 to t − 5, and becomes insignificant ¯ 1 , and Y ¯2 ) when (t + 1)-year controls for sending firm and new workers characteristics (Xt+1 , Y t+1 t+1 are included (columns 3 and 4). Anyway, adding Asi,t+1 does not materially affect the estimates for the productivity gap. Summing up, the results in columns (1)-(4) have two implications. First, that the estimate for ¯ 1 , and Y ¯ 2 are included implies that the contemporaneous Asi,t+1 becomes insignificant once Xt+1 , Y t+1 t+1 link between sending and receiving firms’ productivities can be explained away by controlling for similarities in observed firm and worker characteristics, most importantly by the size of the sending firm. Second, that the inclusion of Asi,t+1 does not change the estimates for the gap’s effect implies that common factors affecting sending and receiving firms’ productivities cannot explain the productivity gap’s effect we have found earlier. The spillover interpretation that we offer in this study is a far more likely explanation.

7.2

Firm-level productivity shocks

The identifying assumption in equation (2) and its extensions is that the error term ε¯j,t+1 is uncorrelated with the productivity gap measure. If however, a firm has experienced a positive productivity shock in year (t − 1), it may affect the firm’s hiring behaviour in year t when the new hires come. For instance, a positive productivity shock can affect the firm’s willingness and ability to hire new workers from more productive firms by offering them more attractive wages. The positive correlation between past productivity shock and the sending–receiving firm productivity gap will induce an upward bias to the estimate of the gap’s effect. Although in our model past productivity shocks are controlled by means of the lags of the dependent variable, as an additional robustness test we apply the estimation procedure developed in Olley and Pakes (1996) which offers extra controls for past productivity shocks. This procedure uses

21

the theoretical fact that a profit-maximising firm will increase capital investment after experiencing a positive productivity shock. Assuming firm i’s current capital stock Kit is equal to the sum of capital from the previous period and capital investments, the profit-maximizing choice of investment, Iit , is described as a function f (·) of the current capital stock and the unobserved productivity shock ωit : Iit = f (Kit , ωit ) Assuming f (·) is monotonic in both capital and productivity, it can be inverted to express ωit as a function of observable investments and capital (all in logs) ωit = g (kit , iit ) As in Olley and Pakes (1996), we approximate the unknown function g with a third-degree polynomial series of capital and investments and include this approximation in equation (2) to control for ωit−1 . The estimation results presented in columns (5) and (6) of Table 10 do not suggest that past productivity shocks affect the choice of firms from which the new hires come, since the estimates for the gap are similar to those obtained earlier from specifications without extra controls for unobserved productivity shocks. That said, these extra controls do help explain the variation in productivity among firms, since most of the terms in the polynomial approximation for function g(·) (skipped) are statistically significant.

7.3

Firm-level fixed effects

Continuing on the issue of identifiability of productivity spillovers separately from other unobserved influences, suppose now that firms do not hire workers randomly but target sending firms based on their characteristics. The possible presence of (long-term) preferences in hiring implies a correlation between the sending and receiving firms’ productivities over time, which the productivity gap may pick up. For instance, a positive correlation, 0.09, between the productivity levels of sending and receiving firms averaged over the length of the sample suggests that firms tend to hire workers from similarly productive firms. Although, by construction, productivity gap cancels common influences to the receiving and sending firms’ productivity, we now include the receiving firm’s fixed effects in the regression as an extra control for their time-invariant productivity determinants. Including the receiving firm fixed effects in equation (1) leads to a reduction of the estimate of the effect of productivity gap by more than a half, from 0.18 to 0.074 (column 7, Table 10). The estimates 22

in Table 10’s column 7 imply that the productivity spillover effect for an average receiving firm hiring 10% of workers from 10% more productive firm falls to 0.07 percentage points per year, suggesting that most of the previously found effect is due to a regression misspecification through failure to account for unobserved productivity components. The reduction in the coefficient on the positive productivity gap is even more pronounced. With these estimates, the mean productivity gain falls to 0.2% per year for an average firm. However, we caution against relying too much on the fixed effects results. The correlation between average productivity levels of the sending and receiving firms is unlikely to account for more than three quarters of the previously reported gap’s effect, since this correlation’s coefficient of 0.09 is much lower than that between the lagged productivity of the sending firm and lead of the receiving firm’s productivity, 0.15 (see Introduction). Moreover, 0.09 is probably an overestimate of the true correlation between the sending and receiving firms’ fixed effects, since, as we have seen earlier, the effect of the gap lasts several periods after a hiring was done and thus affects average productivity over the duration of the sample. In fact, since the fixed effects estimator uses only the within-firm variation in the regression variables for identification, it will ignore any long-term effect of productivity gap. Therefore, the fixed-effects estimate of β is, at best, the lower bound of the true effect, which is still positive and statistically significant.

7.4

Sorting of workers by firm type

One argument against the productivity spillover hypothesis is the sorting of more productive workers into better firms. Workers employed by above-average productive firms can be ex ante more productive than workers from relatively less productive firms, and then the estimated effect of productivity gap may be due to unobservable skills of switching workers. If high-productivity firms provide better compensation for these skills, our human capital measure will control for this alternative interpretation. If, however, the skill is not fully reflected in the salary – for instance, more productive firms can screen high-quality workers better – then the coefficient on productivity gap will be contaminated by the effect of workers unobserved skills. We propose in this section that looking at the effects of the gap averaged for workers with short and long tenure at the sending firms can reveal the importance of unrewarded human capital for the estimate of the productivity gap. Suppose that the worker’s ability to carry knowledge from one

23

firm to another depends on her tenure at the sending firm – the longer, the more knowledge she can absorb and transfer. Screening, on the other hand, takes little time to perform. Then, if the effect of short-tenure workers on the receiving firm’s productivity is as strong as that of longer-tenure workers, it is (mostly) sorting on unobservables that explains the effect of the productivity gap. If, however, the effect of the gap increases with the tenure of switching workers, screening cannot explain it, and we are back to productivity spillovers as the main explanation. Since we do not have information on worker’s employment outside our sample period, the constructed measure of tenure will be censored for early years. For this reason, we focus on the time period between 2001 and 2007, which will enable us to derive a reasonably uncensored tenure variable for the first six years. With this measure, we construct average positive productivity gaps for six groups of new workers who have tenure in the sending firm varying from 1 to 6-plus years, the last group including all observations with censored tenure. Results presented in Table 11 are supportive of the spillovers hypothesis. The effect of the positive productivity gap is insignificant for workers with less than three years of tenure at the sending firm. Admittedly, the insignificant result for the workers with one year’s tenure could be down to a bad worker match which resulted in a quick termination of employment. However, this explanation hardly applies to the workers with two years of tenure for whom the gap’s effect is also insignificant. That the effect becomes significant and increases in magnitude starting at three years of tenure does not support the view that the gap’s effect simply reflects higher quality of workers from relatively more productive firms, pointing instead to the importance of tenure for workers’ ability to absorb knowledge accumulated in their firms.

7.5

Alternative measures of productivity

Until now we have analyzed the effect of knowledge transfers using (wage-adjusted) value added per worker as a measure of firm productivity. Although it is the most widely used measure of labor productivity, it disregards differences in the intensity of capital use which may also affect productivity. In this section we use several alternative measures of productivity, defined relative to a multifactor production technology, which take into account the intensity of factors of production other than labor. With firm level output Q, production technology F, and the vector of input factors X, we can define

24

the total factor productivity (TFP) of firm i at time t as its output net of input factor contributions ait = qit − f (xit )

(10)

where z = ln Z. Using different parametrizations of the production function and estimates of its coefficients, we construct several measures of firm productivity. To remove common influences on productivity of various firms, we normalize the productivity levels calculated with equation (10) by subtracting their averages across firms taken over each industry-year pair. As a first parametrization of F (·) we take the Cobb-Douglas production function with capital, labor, and materials and energy inputs as the factors of production, calculating ait = qit − αk kit − αl lit − αm mit

(11)

We apply a selection of empirical methodologies to estimate the parameters of Cobb-Douglas production technology, αk , αl , αm . Our starting point is the ordinary least squares (OLS) estimator of the production function, which we apply separately for each three-digit industry group in our sample, to allow for differences in input elasticities within the sample. The regression results with TFP estimated through OLS are reported in columns (1) and (2) of Table 12. The results with this alternative measure of productivity are broadly consistent with our benchmark results for value added per worker. The coefficient on the productivity gap and its positive part are positive and statistically significant, while the effect of new workers coming from less productive firms is insignificant. In addition to the OLS estimates of TFP, we employ a two-step semiparametric estimator by Olley and Pakes (1996), the one we used in section 7.2, to control for input factor endogeneity to unobserved past productivity shocks. The TFP measure based on Olley and Pakes-estimated production function is highly correlated with that based on OLS. Consequently, the changes in the estimates of our interest (columns 3-4) are fairly small.10 Next, we use a more general parameterization of production technology F (·) with a full set of input interactions and order terms, known as the translog production function. The translog specification offers a number of advantages over Cobb-Douglas function, most notable of which is the ability to control for the effect of firm size on output by allowing for non-linear effects of factor inputs on output. Estimation results for equations (2) and (4) with productivity measure derived from the translog production function are presented in columns (5) and (6) of Table 10

Alternative estimates of Cobb-Douglas production function, such as Levinsohn and Petrin (2003) and Wooldridge

(2009), yield productivity measures which are very similar to OLS and Olley-Pakes estimates.

25

12. Although the coefficient on the productivity gap becomes insignificant, the effect of hiring from more productive firms is stronger than in columns (2) and (4). Comparing the magnitude of the spillover effects estimated on different measures of productivity, we see that TFP-based estimates are only about half of those in the benchmark specification. For instance, taking the Olley and Pakes (1996) measure of TFP, the estimated productivity gain of a receiving firm from hiring 10% of new workers from 10% more productive firms falls from 0.351% (Table 4) to 0.178%. In fact, however, the two measures productivity produce similar results when we relate the regression estimates to their respective sample moments. Taking the sample average and standard deviation of the gap in value added, 0.024 and 0.1, respectively, the average firm gains 0.024 × 0.351/0.1 = 0.084 standard deviations of the productivity gap by hiring workers from more productive firms. Doing the same calculations with the Olley and Pakes’ measure of productivity gap (average 0.013, standard deviation 0.025) produces a gain of 0.093 standard deviations of the gap. Hence the results for different measures of productivity are closer to each other than simple regression estimates seem to suggest. Finally, in the last six columns of Table 12 we look at the effect of knowledge transfers on the receiving firm’s profitability. Columns (7) and (8) show the results for the gap measured as the difference between the average profitability of sending firms and the profitability of the receiving firm one year before a hiring is done. Consistent with our earlier results, this relationship is positive for all new workers and is stronger for workers coming from more profitable firms. This positive relationship remains broadly the same for other measures of the gap, such as weighted value added per worker (columns 9 and 10) or TFP estimated from the translog production function (columns 11 and 12). Hence, our results are robust to a variety of ways in which the sending and receiving firms’ economic performance are measured.

8

Conclusion

Let us now take stock of our results. Our basic finding is that hiring workers from firms more productive that the receiving firm reduces the productivity gap between the sending and receiving firms. This effect is equivalent to a rise in the productivity of an average firm by 0.8% a year and persists for at least five years after the hiring. We argue that this effect is the evidence for productivity spillovers from more to less productive firms, and we support this argument by ruling out plausible alternative 26

explanations. One such explanation rests on the transfer of human capital, not knowledge, from the receiving firms. However, we do control for the workers’ human capital, both its observed and unobserved parts, and this measure, though important for the receiving firm’s productivity, makes little difference to our spillover estimates. Moreover, the appeal of the human capital explanation is weakened by the lack of the effect of hiring from less productive firms: if it were due to human capital, the effect would have been symmetric. Finally, if the gap’s effect were really due to new workers’ human capital, it would not depend on the workers’ tenure at their previous firms, whereas in fact it does. Another alternative explanation for the link between the productivity gap and the receiving firm’s productivity level could be a correlation between the productivities of sending and receiving firms. This correlation could be long-term, due to hiring practices, or short-term, due to positive productivity shocks allowing firms to attract workers from better performing firms. However, a positive effect of hiring from above-average productive firms exists for startup firms, and of a similar magnitude to that found on the whole sample. We also find that controlling for unobserved shocks to receiving firms’ productivity makes little difference to the gap’s estimated effect, and hence these shocks could not have accounted for any significant part of our results. Although the long-term correlation between the sending and receiving firms’ productivities is much harder to control for, our calculations suggest that it is too weak to explain the gap’s effect. This effect remains significant even when we include the firm fixed effects, despite a downward bias in its estimate that adding fixed effects incurs. In addition to the above results, our other findings offer some intuitively appealing yet useful characterizations of the productivity spillover effect. For instance, we find that the knowledge transferred by workers is to a large extent industry-specific, and that more educated and skilled workers are can transfer more knowledge. Yet, even unskilled workers previously employed at more productive firms can improve the receiving firms’ productivity, implying that the knowledge transferred through hiring is not necessarily sophisticated and/or codified. Labor turnover is the most plausible mechanism for the transfer of such knowledge. It is also a universal mechanism, applicable for the whole economy, which is important for modern endogenous growth models relying on productivity spillovers between more and less productive (large and small) firms (Eeckhout and Jovanovic, 2002; Luttmer, 2007; Atkeson and Burstein, 2010). A continued inquiry into its effects is important for further development of these models, and for deriving economic policy implications from them. 27

References Abowd, J. M., F. Kramarz, and D. N. Margolis (1999): “High Wage Workers and High Wage Firms,” Econometrica, 67(2), 251–334. Atkeson, A., and A. Burstein (2010): “Innovation, Firm Dynamics, and International Trade,” Journal of Political Economy, 118(3), 433–484. Battu, H., C. R. Belfield, and P. J. Sloane (2003): “Human Capital Spillovers within the Workplace: Evidence for Great Britain,” Oxford Bulletin of Economics and Statistics, 65(5), 575– 594. Duguet, E., and M. MacGarvie (2005): “How well do patent citations measure flows of technology? Evidence from French innovation surveys,” Economics of Innovation and New Technology, 14(5), 375–393. Eeckhout, J., and B. Jovanovic (2002): “Knowledge Spillovers and Inequality,” American Economic Review, 92(5), 1290–1307. Gorg, H., and E. Strobl (2005): “Spillovers from Foreign Firms through Worker Mobility: An Empirical Investigation,” Scandinavian Journal of Economics, 107(4), 693–709. Griliches, Z. (1992): “The Search for R&D Spillovers,” Scandinavian Journal of Economics, 94(0), S29–47. Grossman, G. M., and E. Helpman (1991): “Quality Ladders in the Theory of Growth,” Review of Economic Studies, 58(1), 43–61. Hall, B. H., A. B. Jaffe, and M. Trajtenberg (2001): “The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools,” NBER Working Papers 8498, National Bureau of Economic Research, Inc. Jaffe, A. B., M. Trajtenberg, and R. Henderson (1993): “Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations,” The Quarterly Journal of Economics, 108(3), 577–98. Jorda, O. (2005): “Estimation and Inference of Impulse Responses by Local Projections,” American Economic Review, 95(1), 161–182.

28

Kaiser, U., H. C. Kongsted, and Thomas (2008): “Labor Mobility and Patenting Activity,” CAM Working Papers 2008-07, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics. Kim, J., and G. Marschke (2005): “Labor Mobility of Scientists, Technological Diffusion, and the Firm’s Patenting Decision,” RAND Journal of Economics, 36(2), 298–317. Levinsohn, J., and A. Petrin (2003): “Estimating Production Functions Using Inputs to Control for Unobservables,” Review of Economic Studies, 70(2), 317–341. Luttmer, E. G. J. (2007): “Selection, Growth, and the Size Distribution of Firms,” The Quarterly Journal of Economics, 122(3), 1103–1144. Malchow-Moller, N., J. R. Markusen, and B. Schjerning (2007): “Foreign Firms, Domestic Wages,” NBER Working Papers 13001, National Bureau of Economic Research, Inc. Maliranta, M., P. Mohnen, and P. Rouvinen (2009): “Is inter-firm labor mobility a channel of knowledge spillovers? Evidence from a linked employer–employee panel,” Industrial and Corporate Change, 18(6), 1161–1191. Martins, P., and J. Jin (2010): “Firm-level social returns to education,” Journal of Population Economics, 23(2), 539–558. Moretti, E. (2004): “Workers’ Education, Spillovers, and Productivity: Evidence from Plant-Level Production Functions,” American Economic Review, 94(3), 656–690. Olley, S., and A. Pakes (1996): “The Dynamics of Productivity in the Telecommunications Equipment Industry,” Econometrica, 64(6), 1263–1298. Poole, J. P. (2009): “Knowledge Transfers from Multinational to Domestic Firms: Evidence from Worker Mobility,” Working paper. Rao, H., and R. Drazin (2002): Academy of Management Journal 45(3). Romer, P. M. (1990): “Endogenous Technological Change,” Journal of Political Economy, 98(5), S71–102. Song, J., P. Almeida, and G. Wu (2003): “Learning-by-Hiring: When Is Mobility More Likely to Facilitate Inter-Firm Knowledge Transfer,” Management Science, 29(4), 351–365.

29

Wooldridge, J. M. (2009): “On estimating firm-level production functions using proxy variables to control for unobservables,” Economics Letters, 104(3), 112–114.

30

Figure  1.  The  relationship  between  labor  turnover  and  industry-­‐level  TFP  variance  by  3-­‐digit  NACE   industry.    

 

 

Table  1.  Summary  statistics  for  workers  

    Wage  (log)   Age  (years)   Experience  (years)   Male    (share)   High  school  (share)   College  (share)   University  degree  (share)   Unskilled  workers  (share   Skilled  workers  (share)   Managers  (share)   log  value  added  per  worker   Share  in  the  labor  force   Firm  size  (#  of  workers)   Number  of  observations  

 

 

stayers  

new  hires  

new  hires  from   more  productive   firms  

12.23   39.6   14.7   67.7   38.0   49.8   12.2   25.5   65.4   9.2   6.36   0.885   1,105   5,127,165  

12.21   36.9   14.2   72.1   35.7   51.8   12.6   19.4   71.8   8.8   6.36   0.115   1,118   668,034  

12.31   37.9   14.8   72.4   34.9   55.2   9.8   18.6   72.1   9.3   6.40   0.023   1,413   135,006  

Notes:     Summary   statistics   is   calculated   for   all   workers   in   manufacturing   industry   for   the   time   period   1995-­‐2004.  The  share  of  new  hires  from  less  productive  firms  is  0.027.  For  the  rest  6.5%  of  new  hires   the  productivity  of  sending  firm  is  unknown  

Table  2.  Summary  statistics  for  firms        

  Wage  (log)   Age  (years)   Experience  (years)   Male    (share)   High  school  (share)   College  (share)   University  degree  (share)   Unskilled  workers  (share   Skilled  workers  (share)   Managers  (share)   log  VA  per  worker   log  VA  per  worker,     previous  firm   Share  of  new  workers   Firm  size  (#  of  workers)   Number  of  observations  

All  firms  

 

 

No  hiring  

  Hiring  firms  

 

 

stayers  

hires  from   more   productive   firms  

11.9   40.5   9.2   71.9   34.1   55.4   10.4   54.0   41.0   5.0   6.03  

11.8   42.6   9.1   72.4   32.7   57.2   10.0   67.5   28.6   4.0   5.93  

12.0   37.9   9.2   71.2   36.2   52.8   11.0   34.4   59.1   6.5   6.19  

12.1   34.8   9.1   77.5   33.1   55.8   11.1   17.1   75.8   6.9    

12.1   35.7   9.0   74.3   35.1   53.6   11.3   29.5   64.8   5.7    

  10.6   27   173,929  

  0   7   88,806  

    56   85,123  

6.76  

5.65  

3.5    

3.0    

   

hires  from   less   productive   firms  

   

Notes:    Summary  statistics  is  calculated  for  all  workers  in  manufacturing  industry  for  the  time  period  1995-­‐2004.  Statistics   show  average  values  for  different  firms  and  subgroups  of  workers.    

Table  3.  Knowledge  spillovers,  benchmark  results       Productivity  gap    (  β  )     X  (firm  characteristics)  

 

(1)   (2)   (3)   0.199**   0.182**   0.175**   (0.023)   (0.023)   (0.023)         NO   YES   YES  

(4)   0.179**   (0.024)     YES  

Y1  (average  characteristics  of   stayers)  

NO  

NO  

YES  

YES  

Y2  (average  characteristics  of   new  workers)   R2   N  

NO  

NO  

NO  

YES  

0.570   0.575   0.560   100,195   100,195   100,195  

0.560   100,195  

Notes:   All   specifications   include   year   fixed   effects   and   average   size   of   sending   firm   as   additional  controls.  Robust  standard  errors  in  parentheses  are  clustered  at  the  firm  level.   **  significant  at  1%,  *  significant  at  5%.  Time  period  covered  is  1995-­‐2007.  

 

Table  4.  Knowledge  spillovers  from  more  and  less  productive   firms       Positive  productivity  gap  (  βP  )   Negative  productivity  gap  (  βN  )   Positive  productivity  gap  squared   Negative  productivity  gap  squared   R2   N  

(1)   0.351**   (0.054)  

(2)   0.330**   (0.063)  

0.069   (0.042)  

0.012   (0.051)  

        0.560   100,185  

0.034*   (0.015)   0.013   (0.046)   0.560   100,185  

Notes:   All   specifications   include   year   fixed   effects,   average   size   of   sending   firm,   characteristics  of  receiving  firm  (X),  firm-­‐average  characteristics  of  new  and  incumbent   workers  (Y1  and  Y2)  as  additional  controls.  Robust  standard  errors  in  parentheses  are   clustered   at   the   firm   level.   **   significant   at   1%,   *   significant   at   5%.   Time   period   covered  is  1995-­‐2007.  

   

 

Table  5.  Knowledge  spillovers  from  same  and  different  industries       Productivity  gap,  same  industry  (  βsame  )   Productivity  gap,  different  industry  (  βdiff  )   Positive  productivity  gap,  same  industry  (  βP,same )   Positive  productivity  gap,  different  industry  (  βP,diff)   Negative  productivity  gap,  same  industry  (  βN,same  )   Negative  productivity  gap,  different  industry  (  βN,diff  )   Test  βsame    =  βdiff                  ,  p-­‐value   Test  βP,same    =    βP,diff      ,  p-­‐value   Test  βN,same    =    βN,diff      ,  p-­‐value   R2   N  

(1)   (2)   0.231**       (0.032)     0.145**     (0.036)       0.334**     (0.051)     0.210**     (0.070)     -­‐0.002     (0.045)     -­‐0.043     (0.063)   0.017       0.003     0.554   0.558   0.558   99,715   99,715  

Notes:  All  specifications  include  year  fixed  effects,  average  size  of  sending  firm,  characteristics   of   receiving   firm   (X),   firm-­‐average   characteristics   of   new   and   incumbent   workers   (Y1   and   Y2)   as   additional   controls.   Robust   standard   errors   in   parentheses   are   clustered   at   the   firm   level.   **   significant  at  1%,  *  significant  at  5%.  Time  period  covered  is  1995-­‐2007.  Firms  are  considered  to   be  in  the  same  industry  if  they  have  the  same  2-­‐digit  NACE  industry  code.    

 

 

Table  6.  Knowledge  spillovers  by  education  of  moving  workers       Productivity  gap,  high  school  (  βhigh    )   Productivity  gap,  college  (  βcoll    )   Productivity  gap,  university  (  βuniv    )   Positive  productivity  gap,  high  school  (  βP,high    )   Positive  productivity  gap,  college  (  βP,coll    )   Positive  productivity  gap,  university  (  βP,univ    )   Negative  productivity  gap,  high  school  (  βN,high    )   Negative  productivity  gap,  college  (  βN,coll    )   Negative  productivity  gap,  university  (  βN,univ    )   Test    βhigh    =    βcoll                              ,  p-­‐value   Test    βhigh    =    βuniv                            ,  p-­‐value   Test    βP,high    =    βP,coll                    ,  p-­‐value   Test    βP,high    =    βP,univ                  ,  p-­‐value   R2   N  

(1)   0.111**   (0.029)   0.196**   (0.041)   0.284   (0.159)                           0.096   0.283       0.560   100,195  

(2)                 0.277**   (0.046)   0.299**   (0.064)   0.540**   (0.157)   0.015   (0.039)   0.103   (0.057)   -­‐0.043   (0.332)       0.773   0.107   0.560   100,195  

Notes:   All   specifications   include   year   fixed   effects,   average   size   of   sending   firm,   characteristics   of   receiving   firm   (X),   firm-­‐average   characteristics   of   new   and   incumbent   workers   (Y1   and   Y2)   as   additional   controls.   Robust   standard   errors   in   parentheses   are   clustered  at  the  firm  level.  **  significant  at  1%,  *  significant  at  5%.  Time  period  covered  is   1995-­‐2007.  

 

 

Table  7.  Knowledge  spillovers  by  skill  type  of  moving  workers       Productivity  gap,  unskilled  (  βunskilled    )   Productivity  gap,  skilled  (  βskilled    )   Productivity  gap,  managers  (  βmanager    )   Positive  productivity  gap,  unskilled  (  βP,unskilled    )   Positive  productivity  gap,  skilled  (  βP,skilled    )   Positive  productivity  gap,  managers  (  βP,manager    )   Negative  productivity  gap,  unskilled  (  βN,unskilled    )   Negative  productivity  gap,  skilled  (  βN,skilled    )   Negative  productivity  gap,  managers  (  βN,umanager  )   Test  βunskilled    =    βskilled                              ,  p-­‐value   Test  βunskilled    =    βmanager                        ,  p-­‐value   Test    βP,unskilled    =    βP,skilled                  ,  p-­‐value   Test    βP,unskilled    =    βP,manager            ,  p-­‐value   R2   N  

(1)   0.179**   (0.045)   0.180**   (0.030)   0.315*   (0.181)                           0.997   0.329       0.560   100,195  

(2)                 0.221**   (0.080)   0.326**   (0.052)   0.460   (0.451)   0.118   (0.079)   -­‐0.012   (0.048)   0.223   (0.190)       0.267   0.809   0.560   100,195  

Notes:   All   specifications   include   year   fixed   effects,   average   size   of   sending   firm,   characteristics  of  receiving  firm  (X),  firm-­‐average  characteristics  of  new  and  incumbent   workers   (Y1   and   Y2)   as   additional   controls.   Robust   standard   errors   in   parentheses   are   clustered  at  the  firm  level.  **  significant  at  1%,  *  significant  at  5%.  Time  period  covered   is  1995-­‐2007.  

 

Table  8.  Time  persistency  of  knowledge  spillover  effect   Dependent  variable:     Productivity  gap    (  β  )   Positive  productivity   gap  (  βP  )   Negative  productivity   gap  (  βN  )   R2   N  

TFP  in  1  year   (1)   (2)   0.209**       (0.041)       0.536**     (0.113)     0.100     (0.060)   0.591   0.593   45,723   45,723  

 

TFP  in  2  years   (3)   (4)   0.175**       (0.045)       0.513**     (0.109)     0.009     (0.078)   0.526   0.528   45,723   45,723  

          TFP  in  3  years   TFP  in  4  years   TFP  in  5  years   (5)   (6)   (7)   (8)   (9)   (10)   0.173**       0.147**       0.138**       (0.044)     (0.041)     (0.044)       0.538**     0.546**     0.449**     (0.109)     (0.095)     (0.119)     0.065     -­‐0.036     0.002     (0.074)     (0.071)     (0.069)   0.475   0.477   0.434   0.436   0.401   0.402   45,723   45,723   45,723   45,723   45,723   45,723  

Notes:  All  specifications  include  year  fixed  effects,  average  size  of  sending  firm,  characteristics  of  receiving  firm  (X),  firm-­‐average  characteristics  of  new  and  incumbent  workers   (Y1  and  Y2)  as  additional  controls.  Robust  standard  errors  in  parentheses  are  clustered  at  the  firm  level.  **   significant  at  1%,  *  significant  at  5%.  Time  period  covered  is  1995-­‐ 2007.  In  all  specifications  the  sample  is  restricted  to  the  one  used  in  the  estimation  in  columns  (9)  and  (10).  

  Table  9.  Knowledge  spillover  effect  for  new  firms  

 

Dep.  Variable:  

(1)   0.206**   (0.017)          

Productivity  of  sending  firm     Productivity  of  sending  firm  above  industry  average   Productivity  of  sending  firm  below    industry  average   R2   N  

Probability  of  survival  for   at  least  3  years  after  start  

Future  productivity  

 

      0.330   5,421  

(2)           0.316**   (0.029)     0.094**   (0.026)     0.334   5,421  

 

(3)   0.086**   (0.042)   [0.015]               0.022   5,206  

(4)           0.147*   (0.081)   [0.026]   0.030   (0.069)   [0.005]   0.022   5,206  

Notes:   All   specifications   include   year   fixed   effects,   average   size   of   sending   firm,   characteristics   of   receiving   firm   (X),   firm-­‐average   characteristics   of   new   and   incumbent   workers   (Y1   and   Y2)   as   additional   controls.   Robust   standard   errors   in   parentheses   are   clustered  at  the  firm  level.    Marginal  effects  are  in  square  brackets.  **  significant  at  1%,  *  significant  at  5%.  Time  period  covered  is   1995-­‐2007.  Columns  (1-­‐2)  are  estimated  with  OLS;  columns  (3-­‐4)  are  estimated  with  Probit.    

 

Table  10.  Robustness  tests:  Alternative  specifications  of  regression   equation  (2)      

(1)   0.182**   Productivity  gap  (  β  )   (0.024)       Positive  productivity  gap  (  βP  )       Negative  productivity  gap  (  βN  )     Mean  future  productivity  of  sending   0.057*   firms   (0.027)     Mean  future  productivity  of  more   productive  sending  firms       Mean  future  productivity  of  less   productive  sending  firms         X,  Y1,  and  Y2  for  period  (t+1)   NO   Control  for  TFP  shocks  with  investments   NO   Firm-­‐level  fixed  effects   NO   R2   0.570   N   99,875  

(2)       0.325**   (0.054)   0.070   (0.042)       0.076*   (0.037)   0.017   (0.049)     NO   NO   NO   0.575   99,875  

(3)   0.182**   (0.024)           0.032   (0.034)             YES   NO   NO   0.576   98,388  

  (4)       0.393**   (0.053)   0.064   (0.042)       0.052   (0.056)   0.014   (0.050)     YES   NO   NO   0.578   98,388  

 

 

 

(5)   (6)   (7)   (8)   0.182**     0.074**     (0.024)     (0.015)       0.354**     0.080**     (0.053)     (0.028)     0.077     0.043     (0.042)     (0.032)                                                           NO   NO   NO   NO   YES   YES   NO   NO   NO   NO   YES   YES   0.570   0.575   0.560   0.560   100,195   100,195   100,195   100,195  

Notes:  All  specifications  include  year  fixed  effects,  average  size  of  sending  firm,  characteristics  of  receiving  firm  (X),  firm-­‐average  characteristics  of  new   and  incumbent  workers  (Y1  and  Y2)  as  additional  controls.  Robust  standard  errors  in  parentheses  are  clustered  at  the  firm  level.  **  significant  at  1%,  *   significant  at  5%.  Time  period  covered  is  1995-­‐2007.    In  columns  (5-­‐6)  a  third  order  polynomial  of  investments  and  capital  is  included  to  control  for  firm-­‐ level  TFP  shocks  in  period  t.  Columns  (7-­‐8)  include  firm-­‐level  fixed  effects.  

 

 

Table  11.  Robustness  tests:  The  effect  of  tenure  at  sending  firm  on  knowledge   transfers   Positive  productivity  gap  for  workers  with  1  year  of  experience  at  sending  firm   Positive  productivity  gap  for  workers  with  2  years  of  experience  at  sending  firm     Positive  productivity  gap  for  workers  with  3  years  of  experience  at  sending  firm     Positive  productivity  gap  for  workers  with  4  years  of  experience  at  sending  firm     Positive  productivity  gap  for  workers  with  5  years  of  experience  at  sending  firm     Positive  productivity  gap  for  workers  with  6+  years  of  experience  at  sending  firm       X  (firm  characteristics)   Y1  (average  characteristics  of  stayers)   Y2  (average  characteristics  of  new  workers)   R2   N  

0.144   (0.095)     0.177   (0.151)     0.331*   (0.145)     0.275*   (0.140)     0.348*   (0.166)     0.233**   (0.057)     YES   YES   YES   0.570   55,335  

Notes:  All  specifications  include  year  fixed  effects  and  average  size  of  sending  firm  as  additional  controls.  Robust   standard  errors  in  parentheses  are  clustered  at  the  firm  level.  **  significant  at  1%,  *  significant  at  5%.  Time  period   covered  is  2001-­‐2007.  

 

Table  12.  Robustness  tests:  Productivity  spillovers  using  alternative  measures  of  firm-­‐level   productivity           Productivity  gap      (  β  )  

Positive  productivity   gap  (  βP  )   Negative   productivity  gap     (  βN  )     R2   N  

OLS   (2)  

(1)  

Productivity  measure   Translog   Profit   (5)   (6)   (7)   (8)  

OP   (3)  

 

(4)  

 

 

Profit   (9)  

  Profit   (11)   (12)  

(10)  

0.083**  

   

0.093**  

   

0.050  

   

0.059**  

   

0.064**  

   

0.107  

 

(0.028)  

 

(0.035)  

 

(0.026)  

 

(0.018)  

 

(0.022)  

 

(0.072)  

 

 

0.151**  

 

0.178**  

 

0.207**  

 

0.454**  

 

0.199**  

 

0.373*  

 

(0.049)  

 

(0.068)  

 

(0.071)  

 

(0.053)  

 

(0.041)  

 

(0.184)  

 

0.049  

 

0.056  

 

-­‐0.022  

 

-­‐0.109**  

 

-­‐0.110  

 

-­‐0.108  

 

(0.048)  

 

(0.038)  

 

(0.055)  

 

(0.026)  

 

(0.060)  

 

(0.118)  

 

 

 

 

 

 

 

 

 

 

 

 

0.313  

0.313  

0.297  

0.297  

0.332  

0.332  

0.519  

0.502  

0.442  

0.442  

0.444  

0.444  

103,505   103,505   103,505   103,505   103,505   103,505   83,477   83,477   33,944   33,944   34,051   34,051   Notes:  All  specifications  include  year  fixed  effects,  average  size  of  sending  firm,  characteristics  of  receiving  firm  (X),  firm-­‐average  characteristics  of  new  and  incumbent  workers  (Y1  and  Y2)   as  additional  controls.  Robust  standard  errors  in  parentheses  are  clustered  at  the  firm  level.  **  significant  at  1%,  *  significant  at  5%.  Time  period  covered  is  1995-­‐2007.  In  columns  (1-­‐2)   productivity  is  constructed  from  Cobb-­‐Douglas  production  function  estimated  by  OLS.  In  columns  (3-­‐4)  productivity  is  constructed  from  Cobb-­‐Douglas  production  function  estimated  by   Olley-­‐Pakes   methodology.   In   columns   (5-­‐6)   productivity   is   constructed   from   translog   production   function   estimated   by   OLS.   In   columns   (9-­‐10)   productivity   gap   is   constructed   using   wage-­‐ adjusted  value  added  per  worker  (benchmark  measure),  and  in  columns  (11-­‐12)  it  is  constructed  using  Solow  residuals  from  translog  production  function.