Does mobility really raise inventiveness? Evidence from panel data

Lina Ahlin1 and Olof Ejermo2

Abstract

We investigate how individuals’ patent productivity and patent quality are affected by moving

between firms using a large panel of nearly 80% of the population of Swedish inventors linked to

employer-employee data observed between 1987 and 2007. While we initially find a productivity

effect from a move on the labor market, this effect arises primarily due to the first move on the

labor market and seems to be related to moves to non-multinational firms with Swedish

ownership. We further use labor market turnover variables as novel instrumental variables for

mobility to infer causality, but this also turns the mobility effect insignificant. Our interpretation

is that selection is a strong driver of general productivity effects. However, when we switch our

dependent variables to citation-weighted patents to gauge quality, we find a strong positive effect

on quality with a causal interpretation supported by instrumental variable regressions.

Key words: employer-employee data, inventors, mobility, patent productivity, selection, Sweden

1 CIRCLE and the Department of Economics, Lund University, P.O. Box 117, SE-22100 Lund, Sweden,

+46462220473, fax +46462224161, lina.ahlin@circle.lu.se

2 Corresponding author, CIRCLE, Lund University, P.O. Box 117, SE-22100 Lund, Sweden, +46462229650, fax

+46462224161, olof.ejermo@circle.lu.se

1 Introduction

In recent years, labor mobility as a channel of knowledge transfer has received a high level of

attention. If individuals’ mobility raises productivity without lowering it at the origin firm, the

societal returns to knowledge should increase. Thus, if empirical evidence can be generalized to

show such results, policy could simply be directed towards facilitating mobility in order to raise

innovation. On the other hand, if for instance only seasoned inventors or newly educated

individuals experience such effects, policy should perhaps be more targeted towards these

groups. In this paper we try to uncover some of the factors that contribute to higher inventiveness

through mobility.

Lacking other data on the innovativeness of individuals, many papers in the literature focus on

inventors that are listed on patent records. Although inventors are a subset of the population of

individuals contributing to innovative output, their activities are not peripheral in modern

economies: in a small country like Sweden (9.5 million inhabitants), about 1,500 patents are

granted yearly at the European Patent Office (EPO) where total patent filing costs can be

estimated at 75 million euros and this does not even include costs at other patent bureaus such as

the USPTO.

Despite the relative visibility of inventors in data compared to other definitions of innovative

output, no large-scale analysis using population-wide data on inventors has been undertaken to

estimate the effects of mobility on inventiveness. One of the reasons has been lacking

complementary data that can be used to examine and control for individual characteristics and

the possibility to observe how inventors’ patenting behavior changes over time, and as a

consequence the inability to infer a solid general productivity-increasing effect from mobility.

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We use a close to full population sample, consisting of 80 percent of Swedish inventors listed on

applications to the European Patent Office (EPO) that are observed over the period 1990-2007.

Inventors were linked with directories housed at Statistics Sweden on the whole population using

their social security number. These data offer a number of advantages over datasets used

previously concerning the definition of mobility and the time-patterns of inventive behavior. A

first aim of our paper is therefore to uncover how general the productivity effect is following

mobility using panel fixed effects methods. We also try to weed out a causal effect from mobility

using novel instrumental variables based on national and regionally-varying turnover rates by

industry that we show is correlated with inventor mobility, but not directly correlated with an

inventor’s patent productivity.

Knowing if mobility is important for inventiveness is important, but disentangling how any

effects come about is equally important. The current literature has taken several routes to

examine mechanisms leading to productivity effects. Some of the recent studies focus on the

learning effects that arise among the staff at the recipient firm (Singh and Agrawal 2011;

Palomeras and Melero 2010; Song, Almeida, and Wu 2003), disagreement as a source of

spillovers and labor mobility (Mokyr 2006; Klepper 2007, who studies spin-offs), the role of

institutional aspects, such as noncompete agreements (Marx, Strumsky, and Fleming 2009), and

the effect of a move on the productivity of the person who moves (Hoisl 2007, 2009; Lenzi

2009). Our study contributes to the estimation of how the move affects the individuals moving

after their own productivity (inventiveness). Also, while existing studies has had an explicit or

implicit focus on ‘star inventors’, our data allow us to draw conclusions for the population of

inventors at large and in contrast to earlier studies we can employ panel regressions to eliminate

time-invariant fixed effects.

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In our investigation, we examine how productivity effects vary over the life-cycle of the

inventor. Clearly, inventors may self-select into working for an employer who provides

opportunities for learning. Rosen (1972) developed a model, where the main implication was

that individuals seek to accumulate such experience especially early on in their career. While

learning effects have been studied for wage formation (Møen 2005) we add evidence on career

effects by separately analyzing whether productivity effects differ by the order of the move on

the labor market.

Finally, we investigate mobility effects using an alternative productivity measure, citation-

weighted patent counts, a method commonly used to quality-adjust patents. This may shed light

on which individuals become ‘better’ inventors, in the sense of substituting quality for quantity.

Our estimates show that in non-instrumented regressions, there is a positive average productivity

effect following a move that pertains to the first move made on the labor market, in line with

Rosen’s prediction. However, this average effect quickly turns non-significant for two reasons.

First, once we include control variables in addition to year dummy fixed effects we find that the

move effect is absorbed by and explained by moves to Swedish-owned but not multinational

firms.

Second, move-effects are also non-significant once we instrument mobility with turnover rates

that vary by industry for the whole of Sweden and by region. On the other hand, when we instead

employ citation-weighed patents as our dependent variable combined with instrumental variable

methods, we find that the level of quality-adjusted patents substantially increases for inventors

with earlier patent experience following a move. Moreover, this effect does not seem to differ for

different move orders. Therefore, only for specific groups are there any significant mobility

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effects. Policy would therefore be misguided if it tried to stimulate mobility generally. Instead,

there may be a role for policy in trying to stimulate mobility among more seasoned inventors.

2 Literature review

The study of spillovers has been of great interest in economics both empirically (Griliches 1957;

Mansfield 1961) and theoretically (Nelson 1959; Romer 1986). However, pinning down the

importance of spillovers numerically has proved to be difficult, due to its complexity and the

variety of mechanisms involved (Feldman 1999).

In order to estimate the importance of spillovers many researchers use patent data (Griliches

1990). The first studies centered on the geographical (rather than organizational) mobility of

knowledge workers. Jaffe, Trajtenberg, and Henderson (1993) investigated the geographical

reach of spillovers through matched samples of patent citations, controlling for time patterns and

technological similarity. They found that knowledge spillovers initially tended to be

geographically localized, and then subsequently spread over large distances. Further work

recognized that geographical distance may be mitigated by the social network created between

firms from the loss or gain of an employee (Singh 2005; Agrawal, Cockburn, and McHale 2006;

Corredoira and Rosenkopf 2010), although Breschi and Lissoni (2009) found that this knowledge

diffusion is hampered by the reluctance of inventors to relocate spatially

Other studies focus on the individual level and the organizational integration of knowledge from

spillovers at the firm level. The threat of interfirm mobility of inventors sometimes induces firms

to patent strategically with the intention of appropriating as much of the returns to their

innovations as possible, limiting the knowledge spillover that takes place between firms (Kim

and Marschke 2005; Schankerman, Shalem, and Trajtenberg 2006). Other mechanisms include

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noncompete agreements and employee retention contracts to protect innovations (Marx et al.

2009) as well as a reputed toughness in patent litigation (Agarwal, Ganco, and Ziedonis 2009).

These factors may hamper the mobility of inventors and the extent of technological progress

(Cooper 2001).

Studies of worker inflows focus on firms’ ability to obtain knowledge and learn from an inventor

recruited from another firm (Singh and Agrawal 2011; Palomeras and Melero 2010; Song et al.

2003). Song et al. (2003) conclude that knowledge transfers through labor mobility are more

likely if the hiring firm is less technologically path dependent. Palomeras and Melero (2010) find

that the quality of the inventor’s work and the level of complementarity with core competencies

of a firm that an inventor is moving to has a positive impact on mobility. Singh and Agrawal

(2011) identify the spillover effect from citations by combining Jaffe et al.’s (1993) matched

sample approach with the change in citation patterns following a newly recruited inventor’s

patents compared to pre-recruitment patents. Singh and Agrawal (2011) find that a large part of

estimated knowledge spillovers is associated with the newly recruited inventor’s use of his/her

own prior inventions, rather than organizational learning. Maliranta, Mohnen, and Rouvinen

(2009) find that it is primarily workers who move from R&D to non-R&D activities who help

boost the recipient firm’s productivity. Partly corroborating this, Kaiser, Kongsted, and Rønde

(2015) investigate how mobile R&D workers affect the patenting activities of both the departed

and the recipient firm. They find that the effect is stronger if the firm that the R&D worker

leaves is already patent active.

Similar to us, others focus on the subsequent patent productivity of the mobile inventor.

Trajtenberg and Shiff (2008) find that inventors who hold higher-quality patents are more mobile

than inventors producing lesser-quality patents. Furthermore, the patents these inventors produce

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after moving are of higher quality. This suggests a positive relationship between patent quality

and mobility, although no causal link is established. Team experience is further found to exert a

negative impact on mobility, perhaps due to network effects or good matching. Schettino,

Sterlacchini, and Venturini (2013) find that patent quality is associated with the age of the

inventor, being male, having higher education as well as working in teams, although the

individual characteristics are not found to influence patent productivity.

Trying to determine causal effects rather than just correlations, Hoisl (2007) investigates whether

mobility and productivity are endogenously determined and looks at the causal relationship

between the two for a survey dataset of German inventors. Using two sets of instrumental

variable estimations, she finds a simultaneous relationship in which mobile inventors are more

productive than their immobile counterparts but also that an increase in productivity reduces the

likelihood of a move. As instruments for mobility, she includes incentives for inventive activities

(taken from a questionnaire), the technical concentration of patents, and the size of the region in

which the inventor works. The instruments for productivity are related to external sources of

knowledge, that is, to the extent that inventors use patent documents and scientific literature to

get input rather than through personal interaction, and are also taken from a questionnaire.

However, difficulties in collecting comprehensive data place limitations on the generalizability

of the study by Hoisl (2007). First, the study uses only inventors with at least two patent

applications, although other studies indicate that most inventors have only one patent

(Trajtenberg, Shiff, and Melamed 2006; Ejermo 2011). Second, the inventors analyzed by Hoisl

(2007) have an average patent productivity of 14.7, which means it is highly skewed toward the

more inventive part of the distribution.

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In a follow-up study using a matched sample approach, Hoisl (2009) determines whether

mobility affects high- and low-productivity inventors differently, using jointly estimated quantile

regressions. She finds that inventors who are initially more productive benefit more, in terms of

their patents receiving more citations from changing jobs than those who are initially less

productive. Her results also suggest that inventors who are poorly matched to their employers

tend to move to increase their productivity. Lenzi (2008) tries to overcome the potential high-

productivity bias stemming from only using patent records to determine mobility by

complementing patent documents with curriculum vitaes. She examines Italian inventors active

in the pharmaceutical industry to determine whether interfirm mobility effects differ for these

two types of records. In effect, important knowledge can be gained at one job although no patent

is applied for or is kept secret by the inventor, and this knowledge is subsequently used to file a

patent at another firm. She finds that patent documents often underestimate the number of moves

that inventors actually undertake over the course of their careers as well as specify the affiliation

of the inventors incorrectly. Using Poisson regressions, the results suggest a significant positive

effect running from mobility on patent documents to both productivity and the number of

citations that each patent of the inventor receives, as well as from productivity to patent

document mobility (i.e., in the opposite direction). Basing instead mobility on CVs, she finds an

insignificant effect on productivity and a positive significant effect on the number of citations

each patent receives. Also, the effect runs from productivity toward mobility using CVs. These

results cast doubt on the possibility to infer mobility from patent documents. Lenzi (2009) uses a

duration analysis and incorporates more controls. The results indicate that life-cycle effects,

inventive productivity, and the geographical location in which the inventor works are drivers of

mobility. It is mainly the most productive inventors who are likely to move. Nevertheless, the

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results are largely consistent with Hoisl (2007), at least when only considering patent documents,

in that mobility seems to be a mechanism that improves the matching between inventors and

employers, which increases productivity. However, due both to the small sample size (106

inventors in one industry surveyed) and the absence of the possibility of causally establishing

effects, the conclusions may not be generalizable.

Ge, Huang and Png (forthcoming) investigate the extent to which patent data alone misclassifies

mobility by studying inventors’ LinkedIn profiles as well as conducting a small survey to verify

their results. They find that the accuracy of patent data is 70 % or less, whereas LinkedIn

provides 90 % or higher accuracy. Patent data tends to provide a high degree of false negatives,

i.e. failing to observe mobility, and a smaller degree of false positives (i.e. observing mobility

when there is none). Furthermore, their results suggest that the bias is larger for inventors who

patent less frequently or have shorter careers. Running several regressions with mobility as the

dependent variable, they show that the patent rate negatively affects mobility - i.e. the same

results as found in Hoisl (2007).

In sum, existing studies have typically found a significant and positive effect of interfirm

mobility on productivity (regardless of whether they consider causal effects), but a negative

effect running from productivity to mobility.

Another aspect that may influence the effect of mobility and patent productivity concerns when

the move takes place over the life-cycle of individuals. Rosen (1972) developed a model of

human capital accumulation according to which individuals choose between different learning

opportunities through job hopping. Later in their career, they capitalize on their endowments of

human capital. The length of time over which they benefit from their human capital therefore

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becomes critical. Early in their careers, individuals prefer jobs with much learning opportunities

relative to their wage level over jobs with low learning opportunities relative to wages. An

observed higher patent productivity may therefore also reflect a conscious choice to work at a

firm that provides ample learning opportunities early in the career. Møen (2005) found empirical

support for this model in that the wage development of Norwegian engineers was slow at the

beginning of the career, suggesting instead that they consciously chose low wages in favor of

high learning opportunities. Following these ideas, in this paper we examine if there are

differences in productivity effects over the careers, by separately analyzing different move

orders, expecting a stronger learning effect for first-time movers.

Together, the above studies suggest that selection may be an important part of the reason a

productivity effect differs following a move. For instance, Hoisl (2007) found that mobility was

slowed if an individual patented more. Thus, selection may operate both in terms of the selection

into mobility and with respect to the outcome itself.

3 Data

We have stressed the advantage of adopting a longitudinal perspective for the study of mobility-

effects. Our dataset encompasses slightly fewer than 23,000 inventors, where 80 percent of all

inventors listed on EPO records with Swedish addresses have been identified. Our matching

procedure was as follows. At first, a commercial firm added the Swedish social security number

(the “personnummer”, SSN) for a large part of the inventor records. This firm was, however,

only able to link inventors whose addresses were either a) changed during the last three years at

the time of linking (2011) or b) whose addresses had stayed unchanged over time. If we had

stopped there, the panel would have been plagued by lack of mobility and ensuing selection

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biases. We therefore undertook a highly laborious effort to add inventor social security numbers

back in time. We extracted name-address records from the DVD “Sveriges befolkning 1990”

(Sweden’s population in 1990) (Sveriges släktforskarförbund 2011) that hold records of the

population, including their addresses, and found and validated best name-address combinations

using the Levenshtein algorithm.

Name matches together with birth date could then be used to find the SSN, which produced a

match rate for the whole period that was fairly even. In anonymized form, this material was

further linked to individual employer-employee registers at Statistics Sweden. These data enable

further description of inventors’ demographic and education characteristics, wage information,

data on workplace, and associated firm and business group information. Demographic and

education characteristics and developments over time of Swedish inventors have been described

in Ejermo (2011) and Jung and Ejermo (2014). Another appealing feature of the data is their link

to DEE (The Dynamics of Enterprises and Establishments) data provided by Statistics Sweden,

whereby organizational restructuring (such as mergers and acquisitions) or change of

organizational form does not affect the firm identifier. This makes the firm id stable over time

and enables an accurate characterization of mobility across organizational entities.1

We determine whether inventor moves take place across organizational boundaries based on the

following criteria:

(1) an individual in t+1 has a different establishment, a different firm, and a separate corporate

group from that observed in t; and

(2) both the organization that the inventor leaves and the one the inventor enters are observed in

both t and t+1.

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It could be noted that about half of all inventors’ moves to a new establishment involve within-

firm or within-business group moves that we judge not to be inter-organizational and hence were

excluded from the analysis. Note also that (2) implies that moves due to firm exits are excluded.

This choice is motivated because such moves may not be voluntary from the inventor’s

perspective and the inclusion may induce a negative productivity bias if the underlying reason

for the firm exit correlates with inventor patent productivity. The assumption also implies that

moves to firms that start up in the same year are excluded. This choice is based on the motivation

that we are investigating the effects of labor mobility and not spin-offs.

The dataset also includes information about the inventor, such as birth year (which can be used

to calculate age), educational field and level as well as yearly observations of establishment and

firm affiliation, which in addition to mobility are used to create tenure variables. The firm data

are also connected to the corporation register, and include information on firm ownership, i.e.

whether owned by a Swedish or foreign mother firm and whether being a multinational firm.

Appendix A describes the rationales for the inclusion of the various control variables.

We study mobility over the 1987-2007 period where it can be noted that inventor characteristics

are also observed in years when patents are not filed. Our study is thus based on the evolution

over time of inventive activity and associated mobility across firms at the individual level.

In order to more precisely delineate the first move on the labor market, we require inventors not

to be more than 30 years old when they first appear in our data. We also exclude inventors under

the age of 21 and consistently use this material in our analyses. These restrictions imply that we

retain up to 10,481 inventors observed in unbalanced panels in the analyses.

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For our regressions, we examine the effect of separate moves in the career. Our treated

individuals therefore have experienced a move of order n, with n=1,…,4, while the control group

consist of individuals who moved n-1 times.

The “treatment” of inventors thus consists of the effect of a move on patent productivity. If a

move takes place in t, a mobility dummy is created that takes the value of zero prior to any move

and one in t+1,…,T, where T marks the final year the inventor works at the new firm. In our data,

firm affiliation is recorded only once a year, in November.2 We use t+1 to ensure that the move

has been finalized and further distinguish the order of moves by constructing different dummy

variables “Move1”, “Move2”, “Move3”, and “Move4”. Moves are, however, only observed

1987-2007, but our 30-year age restriction should make these labels more accurate. Our

dependent variable Patent productivity is calculated using the sum of the share (or fractions) of

applied patents that our focal inventor contributes to in a given year. For instance, if the focal

inventor had two patents applied for in year t co-invented with one other person on the first

patent, and three other inventor on the second patent, the productivity measure is ½ + ¼ = ¾ in t.

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INSERT TABLE I ABOUT HERE

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Table I shows descriptive statistics of inventors in the year they enter into the sample for the first

time. The number of moves undertaken over their observation period only averages 1.7 but

ranges from 0-12; number of patents applied for is 1.6 on average for an individual measured as

sum fractions over time as of above; 89% are male inventors. A large share is highly educated:

26 percent have bachelor degrees or above and an additional 41 percent has some technical

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education. Probably because of their long education, inventors are on average 25 years when first

observed. Most have engineering types of education; 67 percent have technical education

backgrounds, but only 9 percent have natural sciences background, and 3 percent have social

sciences or medicine education background. A large share of inventors, 42 percent, work in

Swedish-owned business groups with foreign subsidiaries. Another 37 percent work for Swedish,

non-business group firms; 11 percent for Swedish business groups without foreign subsidiaries,

and another 9 percent for foreign-owned business groups with Swedish subsidiaries. The most

common-size category for the firm they work in is a firm with 1501-5000 employees. Other

categories are fairly evenly populated at 9-14 percent, except for 251-500 employee sized firms

with only 6 percent, and more than 50,000 employees where only 2 percent of inventors work.

Ten percent of inventors work in the public sector when first observed.

Figure 1 depicts average cumulative inventor productivity before and after mobility for moves 1-

4. Of course, it is to be expected that cumulative productivity is to be higher in connection with

higher order moves, as shown in the graph as the order also correlates with age. We note that the

patent productivity of inventors rises with a steeper slope after the first move. Inventiveness also

rises after other moves, but the graph suggests that this rising productivity is part of a long-term

trend and not attributed to the move per se. A second observation concerns the shape of the

cumulative productivity line. There is no sign that inventiveness rises suddenly after a move, as

would be expected if inventiveness rose directly from knowledge transfer. Instead, inventiveness

rises for each year following the move, compared to before the move. A “before-and-after”

perspective on the perceived mobility effect is therefore a valid way of characterizing patent

productivity developments and suggests that difference-in-difference (DD) estimations would be

the most relevant starting point for regression analyses as we have also implicitly assumed above

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in our definition of mobility. Of course, no causal interpretation can be given to these

observations. For instance, productivity is higher for individuals before and after higher-order

moves, because part of the accumulated patent productivity is related to inventor age and firm

strategy. In any case, the figure suggests that the order of moves is warranted and in line with

Rosen’s learning hypothesis (1972).

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INSERT FIGURE I ABOUT HERE

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The average productivity level in our panel of 161,607 year-individual observations of inventors

is close to 0.07, i.e. 7% of a single-inventor patent, reflecting that patenting is relatively rare and

that most individuals are listed only once as inventors on patents. Table II shows the distribution

of observations by move and cumulative productivity. Inventiveness is even lower than the

average before the first move, at about 0.059 on a yearly basis, a value that rises to 0.082 after

one move. This figure rises slightly to 0.089 following the second move. Mobility is also

relatively rare; we find that mobility occurs only in about 10% of the individual year-

observations. Of these, 44% of the moves concern the first move, 28% the second, 15% the third,

7% the fourth, and less than 6% of moves represent higher-order moves.

Does previous inventiveness influence whether an individual chooses to change jobs? Table II

examines the distribution of our sample with respect to patenting productivity and accumulated

mobility. Note that in the upper panel, the distribution for every observation is shown, whereas in

the bottom panel, data only show productivity just before a move. It is clear that the vast

majority of individuals have no patents before any of their moves. It is also clear that the share of

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individuals with no patent is higher before the actual move is undertaken. For instance, in 69% of

observations no patenting is observed before their first move. Examining this figure in the

bottom panel, we find that 82% of movers had no patenting. Corresponding figures for the

second move are 57% and 69%. Thus, we can at least descriptively corroborate the finding by

Hoisl (2007) that patenting seems to slow down mobility. The opposite is also true: less patent-

productive individuals are more likely to change jobs, leading to a selection into mobility.

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INSERT TABLE II ABOUT HERE

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4 Estimation strategy

Our econometric specification involves estimating the effect on an inventor’s patenting

propensity from moving to a new firm based on differences-in-differences (DD). The main

identifying assumption is that there should be no difference between the treated and the control

group in the absence of treatment (mobility). As such, we estimate an equation of the type:

y¿=β1 D¿+ β2' X¿+ β3

' F¿+δ t+θi+ε¿ , (1)

where y¿ represents patent productivity of individual i in period t modeled as a function of X, a

set of (partially time-varying) inventor characteristics and a set of firm characteristics, F. θ

denotes inventor fixed effects, δ t are time fixed effects, and ε is a classical error term. Time

fixed effects pick up variation in patenting that is due to time trends and right-censoring of our

data. The binary variable, D¿ , D¿+1 , D¿+2 ,… , D¿, takes the value one if the move took place in t-1

and stays at one for the duration of employment at the new employer where the final observation

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is at T. T may also denote the last year of observation of an individual in the dataset. Note that a

move to a new employer observed in T+1 is recorded in a new dummy variable, labeled

according to move order, and constructed in the same manner. Hence, β1 is the estimated

coefficient of interest. Because the DD model is essentially a fixed effect model, time-invariant

characteristics such as gender or high school grades (to control for ability) cannot meaningfully

be included. Among the control variables (X and F) we include the variables presented in

Appendix A. The coefficients of these variables are only presented in our first table with

regressions to save on space.

Although the panel structure of our data allows us, through fixed-effects regressions, to remove

time-invariant heterogeneity, several potential issues warrant an instrumental variable approach.

Fixed-effects regressions do not deal with time-varying unobserved heterogeneity. The ability of

an inventor may change over time, for instance, due to favorable work conditions, helpful

colleagues, better networks, or other unmeasurable circumstances. Thus, while ability is to some

extent path dependent, it is not deterministically given and therefore not entirely captured by

fixed effects. Also, endogeneity may be caused by reverse causality. While mobility may raise

productivity, productivity may also affect mobility, because more able inventors self-select into

seeking better job opportunities (Hoisl 2007). One such example is from Klepper’s (2007) work,

which highlights how entrepreneurs with different ideas (abilities) choose to leave their present

employer. Instrumental variable techniques can deal with these issues by isolating variation that

give rise to a causal interpretation from the variable of interest. Two recent papers by Fabian

Waldinger (2010, 2012) employ instrumental variable techniques to DD estimation in a setting

somewhat related to ours. He uses the dismissal of Jewish scientists from universities in Nazi

Germany in the 1930s as an instrument to estimate the effect of the loss of a supervisor on a PhD

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student’s scientific productivity (Waldinger 2010) and on faculty peer effects (Waldinger 2012).

He distinguishes a long-term negative effect in the first case but not in the second case.

As instruments for mobility, we use the annual employment growth rate in the sectors of an

individual’s work in a specific year in four versions: 1) Employment growth total in sector, 2)

Employment growth total in sector in region, i.e. the same variable but measured on the regional

level, 3) Highly educated employment growth total in sector, and 4) Highly educated

employment growth in sector in region. Sectors are given by the 2-digit NACE code and

“region” reflects one of 72 labor commuting regions in which an individual works according to

Tillväxtverket (2005). Highly educated are individuals with at least two years of higher

education.

Our instruments should capture opportunities for a job change that derive from regional size,

time-varying business conditions, and specific opportunities in the industry in which an inventor

works. We argue that such fluctuations, which vary by industry and level of education, provide

exogenous variation to affect individual’s mobility decisions but not directly their engagement in

patenting. Table III shows the correlation between our main dependent variable, patent fractions,

different dummies for move order and our instrumental variable. We exclude all control

variables used in the regressions for space reasons, except for Tenure to illustrate its relation to

mobility and productivity.

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INSERT TABLE III ABOUT HERE

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The first column shows that move variables generally have some correlation with patent

fractions. Also, the instrumental variables have a very low correlation with fractions, giving

basic support for their use as instruments. Moreover, the instruments have some correlation with

mobility on the individual level, especially those based on the region. But the table does not

provide a definitive answer as to whether instruments are strong enough to explain some

variation in our mobility variables and whether they pass the exclusion restriction tests. Also, we

need to have a preparedness to exclude combinations of instruments that explain much of the

same variation. Clearly they have different explanatory power with the most specific variable

Highly educated employment growth in sector in region generally exhibiting the highest

correlation with moves of any order.

5 Regression results

5.1 Effects on patent productivity

Table IV shows initial non-instrumented panel fixed-effect regressions without control variables

(except for year dummies), for different move orders. In the first column (model A0) any move

is considered. It shows that there is a general highly statistically significant move effect. The

other columns illustrate that this effect, just as we saw indications of in Figure 1 and the previous

Tables, is explained by the first move (model A1) which is responsible for the productivity effect

while moves 2-4 (models A2-A4) are not statistically significantly different from zero.

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INSERT TABLE IV ABOUT HERE

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INSERT TABLE V ABOUT HERE

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In Table V we re-run our different move order regressions adding the control variables.

Including them also makes the first order move (model B1) and any move (model B0)

insignificantly different from zero. After examining which control variables that render this

move effect insignificant by excluding them one-by-one, we discover that if we exclude

ownership category dummies the effect turns significant and of similar order again. We then

interact these with the move effect to find that it is the variable “Swedish owned without foreign

subsidiaries” that explains the loss of significance.3 In other words, inventors’ productivity

effects result from moving to such firms. We also checked whether certain moves, e.g. from

Foreign owned or not would be important, but found that this effect was general. That is, it is not

whether an individual moved from a specific firm type, but to the found category that was

important.

With respect to the control variables, we do not put too much focus on them and will therefore

not report on them in further tables, the main reason being that the fixed-effect panel structure

absorbs much of their variation, and many of them are changing slowly on the individual level.

However, we note from Table V that changes in the Tenure variable has a positive and

significant relationship with patent productivity as has Firm lagged patenting. Moreover, Age

has an inverted U-shaped relationship consistent with studies on life cycle productivity (Levin

and Stephan 1991; Jung and Ejermo 2014). We also note that compared to the baseline category

Foreign business group Swedish subsidiary, the other business group categories exhibit

20

significantly less patenting. It is interesting to note that firms in size categories 11-500 (and also

in 5001-10000) all display significantly lower patenting than those in the smallest baseline (0-10

employees), while those inventors employed in the largest category (> 50000 employees) show

an increased tendency to file patent. Of the different education variables, it seems that obtaining

Some tertiary education is associated with less patenting, whereas this is not the case for the

highest level of education. It is of course hard to draw conclusions from them as they refer to

changes on the individual level. Finally, for an inventor that changes status to the public sector,

the level of inventive activity is significantly negatively affected.

-------------------------------------------

INSERT TABLE VI ABOUT HERE

-------------------------------------------

The next set of regressions instrument the different move variables. Although our instruments are

not the same, they pick up joint variation because, as explained earlier they use the same sector

variable in which the individual works as a base and because business cycles and opportunities

for different types of labor in a sector are correlated. In each regression we initially included all

four instruments and then removed the least significant variable until the regressions passed

Hansen’s J-test for overidentification. Generally this happened with three instrumental variables,

but we present the two-instrument regressions in Table VI along with the first-stage estimates,

because the Kleibergen-Paap (KP) F-statistics for weak instruments tended to be higher and

should lead to less risk of bias in the estimated move effect. As can be seen from the table, all

regressions exhibit high F-statistics and non-rejected Hansen tests. Thus, our instruments are not

21

weak according to the statistical tests and jointly explain sufficient variation in the explanatory

variable.

The IV results show one simple message: no significant effects remain, neither for the average

move nor for each move considered separately. Our explanation for this pattern is that selection

is the driving factor and that we are able to remove these selection effects. These selection

patterns seem to consist of first move effects and moves to Swedish owned firms without foreign

subsidiaries.

An additional check was made to see whether the choice of using patent fractions mattered for

the results by using whole patent applications and whole patent grants instead as our dependent

variable.4 Such changes had no impact on the qualitative results.

5.2 Moves and the quality of patents

Our main results so far seem to differ from those of Hoisl (2007), in that we find no indication of

a causal effect stemming from a move. One might ask whether there are other dimensions of

patenting that are important for inventors, for instance whether inventors who move produce

“better” patents. We check whether quality is affected in our regressions by using citation-

weighted patents (as in Hoisl, 2009), which has commonly been used as an indicator of patent

quality (cf. Trajtenberg 1990; Dahlin and Behrens 2005; Trajtenberg and Shiff 2008).5 Our

quality measure multiplies our previously used dependent variable by number of citations

obtained within three years from application as defined by the OECD (Squicciarini, Dernis, and

Criscuolo 2013).

22

Since it is only meaningful to look at changes in quality for previous patenters, we run

regressions where we restrict the sample to make sure that the inventors had some patenting

before their move. Because this restriction is different depending on move, we run regressions

for each move separately and present the results in Table VII.

-------------------------------------------

INSERT TABLE VII ABOUT HERE

-------------------------------------------

Without instruments, we find a negative effect on patent quality from mobility. However, based

on our earlier results, we are quite suspicious of this result, because it may rather reflect selection

into treatment. Indeed, when using instrumental variables results differ substantially and change

the coefficients to become positive. Due to the restriction that inventors have to have patented

before they move, we lose quite a few observations. It is therefore not too surprising to find that

third and fourth moves are not significantly different from zero. Moreover, those coefficients

vary more when we change the number of instruments. For the first and second move, we find a

strong positive and statistically significant effect that is not very sensitive in size and significance

level to the number of instruments. On net, our conclusion is that there is a positive effect of

moves on patent quality measured by patent citations that does not seem to differ by move order,

although we cannot rule out that it is zero for later moves.

23

6 Conclusion

In a large panel of Swedish inventors, we initially find evidence that patent productivity rises

following a move on the labor market. This general result stems from the first move on the labor

market. However, this result does not hold after inclusion of control variables and once we

instrument our mobility variable. In particular, moving to a Swedish firm without foreign

subsidiaries seems to be responsible for the general effect. But we find no statistically significant

evidence that mobility has a causal effect on patent productivity from our instrumental variable

regressions. Our results are robust to a number of specifications and alternative productivity

variables. In other words, general mobility-reinforcing policies are unlikely to foster a higher

level of inventiveness. They therefore stand in contrast to previous literature that indicates that

inventors generally tend to become more patent productive after a move (cf. Hoisl 2009; Lenzi

2008).

The results differ markedly when we consider quality using citation-weighted patent productivity

together with instrumental variables in our regressions. Here, in line with Hoisl (2009), we find

that quality improved quite substantially and the results indicate that the effect is not different by

move order. The quality effect is also in line with results found by Trajtenberg and Shiff (2008).

Thus, for more experienced inventors, it is perhaps not the quantity of patenting that is important

to consider but the quality. This result also indicates a role for policy in promoting the mobility

of highly inventive individuals. Such policies to foster their mobility may counteract the inherent

lack of mobility that we see appearing after an individual patents.

The results differ from the previous literature for several possible reasons. First, our dataset is

more general in the sense of capturing nearly all inventors in Sweden, than previously adopted

24

datasets in the literature. Moreover, we do not rely on survey data to supplement the patent data.

Second, we do not derive firm affiliations based on patent data but, rather, use register data,

which in our view allows an improvement in precision by providing us with annual data on firm

affiliations, from which we define mobility. Third, we make use of the panel structure of our

dataset, which allows us to use fixed effects at the individual level to control for time-invariant

heterogeneity, such as ability, which could in turn affect productivity.

The present study suggests a number of promising research directions. It would be interesting to

see whether our results translate to other contexts, preferably where individuals move more

easily in the labor market than they do in the Swedish setting. In addition, the role of

agglomerations in productivity enhancing labor mobility is an interesting research avenue. Such

an approach may well be coupled with an effort to disentangle how the characteristics of the

individual match with the properties of the firm in relaying any geographically induced

productivity effects with the intuition that urban agglomerations provide more precise matching

opportunities.

Finally, one might want to examine the extent to which coworkers are positively affected

through a peer effect or estimate effects on others who do not work directly on the same patents

as the moving inventor, either at the firm the inventor leaves (Kim and Marschke 2005) or at the

recipient firm (cf. Agrawal, McHale, and Oettl 2014).

25

NOTES

26

References

Agarwal, Rajshree, Martin Ganco, and Rosemarie H Ziedonis. 2009. Reputations for toughness

in patent enforcement: Implications for knowledge spillovers via inventor mobility.

Strategic Management Journal 30, no. 13:1349-1374.

Agrawal, Ajay, Iain Cockburn, and John McHale. 2006. Gone but not forgotten: Knowledge

flows, labor mobility, and enduring social relationships. Journal of Economic Geography

6, no. 5:571–591.

Agrawal, Ajay, John McHale, and Alex Oettl. 2014. Why stars matter. Working paper no. 20012,

National Bureau of Economic Research Cambridge, MA.

Becker, Gary S. 1975. Human capital: A theoretical and empirical analysis, with special

reference to education. 2nd ed. New York: Columbia University Press.

Breschi, Stefano, and Francesco Lissoni. 2009. Mobility of inventors and networks of

collaboration: An anatomy of localised knowledge flows. Journal of Economic

Geography 9, no. 4:439-468.

Cooper, David P. 2001. Innovation and reciprocal externalities: Information transmission via job

mobility. Journal of Economic Behavior & Organization 45, no. 4:403-425.

Corredoira, Rafael A, and Lori Rosenkopf. 2010. Should auld acquaintance be forgot? The

reverse transfer of knowledge through mobility ties. Strategic Management Journal 31,

no. 2:159-181.

Dahlin, Kristina B, and Dean M Behrens. 2005. When is an invention really radical?: Defining

and measuring technological radicalness. Research Policy 34, no. 5:717-737.

Ejermo, O., and T. Schubert. 2014. Do higher wages reduce inventors’ job turnover? The role of

utility, status and signaling effects. In DRUID summer conference. Copenhagen.

Ejermo, Olof. 2011. Svenska uppfinnare – nytt datamaterial och ny inblick i

innovationsprocessen [Swedish inventors – new data and insights into the innovation

process]. Working paper, Entrepreneurship Forum, Stockholm.

Feldman, Maryann. 1999. The new economics of innovation, spillovers and agglomeration: A

review of empirical studies. Economics of Innovation and New Technology 8, no. 1-2:5-

25.

Ge, Chunmian, Ke-Wei Huang, and Ivan P.L. Png. Forthcoming. Engineer/scientist careers:

Patents, online profiles, and misclassification bias. Strategic Management Journal.

Griliches, Zvi. 1957. Hybrid corn: An exploration in the economics of technological change.

Econometrica 25, no. 4:501-22.

———. 1990. Patent statistics as economic indicators: A survey. Journal of Economic Literature

28, no. 4:1661-1707.

Harhoff, Dietmar, Frederic M. Scherer, and Katrin Vopel. 2003. Citations, family size,

opposition and the value of patent rights. Research Policy 32, no. 8:1343-1363.

Hoisl, Karin. 2007. Tracing mobile inventors-the causality between inventor mobility and

inventor productivity. Research Policy 36, no. 5:619-636.

———. 2009. Does mobility increase the productivity of inventors? The Journal of Technology

Transfer 34, no. 2:212-225.

Jaffe, Adam B., Manuel Trajtenberg, and Rebecca Henderson. 1993. Geographic localization of

knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics

108, no. 3:577-598.

28

Jung, Taehyun, and Olof Ejermo. 2014. Demographic patterns and trends in patenting: Gender,

age, and education of inventors. Technological Forecasting and Social Change 86, no.

July:110-124.

Kaiser, Ulrich, Hans Christian Kongsted, and Thomas Rønde. 2015. Does the mobility of R&D

labor increase innovation? Journal of Economic Behavior & Organization 110, no.

February:91-105.

Kim, Jinyoung, and Gerald Marschke. 2005. Labor mobility of scientists, technological

diffusion, and the firm's patenting decision. RAND Journal of Economics 36, no. 2:298-

317.

Klepper, Steven. 2007. Disagreements, spinoffs, and the evolution of detroit as the capital of the

US automobile industry. Management Science 53, no. 4:616-631.

Lenzi, Camilla. 2008. Technology mobility and job mobility: A comparative analysis between

patent and survey data. In ZEW Conference on the Economics of Innovation and

Patenting. Mannheim.

———. 2009. Patterns and determinants of skilled workers’ mobility: Evidence from a survey of

Italian inventors. Economics of Innovation and New Technology 18, no. 2:161-179.

Levin, Sharon G., and Paula E. Stephan. 1991. Research productivity over the life cycle:

Evidence for academic scientists. The American Economic Review 81, no. 1:114-132.

Maliranta, Mika, Pierre Mohnen, and Petri Rouvinen. 2009. Is inter-firm labor mobility a

channel of knowledge spillovers? Evidence from a linked employer–employee panel.

Industrial and Corporate Change 18, no. 6:1161-1191.

Mansfield, Edwin. 1961. Technical change and the rate of imitation. Econometrica: Journal of

the Econometric Society 29, no. 4:741-766.

29

Marx, Matt, Deborah Strumsky, and Lee Fleming. 2009. Mobility, skills, and the Michigan

noncompete experiment. Management Science 55, no. 6:875-889.

Mokyr, Joel. 2006. Mobility, creativity, and technological development: David Hume, Immanuel

Kant and the economic development of Europe. In Kolloquiumsband of the xx. Deutschen

kongresses für philosophie, ed. G. Abel. Berlin: German Association of Philosophy.

Møen, Jarle. 2005. Is mobility of technical personnel a source of R&D spillovers? Journal of

Labor Economics 23, no. 1:81-114.

Nelson, Richard R. 1959. The simple economics of basic scientific research. The Journal of

Political Economy 67, no. 3:297-306.

Palomeras, Neus, and Eduardo Melero. 2010. Markets for inventors: Learning-by-hiring as a

driver of mobility. Management Science 56, no. 5:881-895.

Romer, Paul M. 1986. Increasing returns and long-run growth. Journal of Political Economy 94,

no. 5:1002-1037.

Rosen, Sherwin. 1972. Learning and experience in the labor market. Journal of Human

Resources 7, no. 3:326-342.

Schankerman, Mark, Roy Shalem, and Manuel Trajtenberg. 2006. Software patents, inventors

and mobility. Working paper, Tel Aviv University, Tel Aviv.

Schettino, Francesco, Alessandro Sterlacchini, and Francesco Venturini. 2013. Inventive

productivity and patent quality: Evidence from Italian inventors. Journal of Policy

Modeling 35, no. 6:1043-1056.

Singh, Jasjit. 2005. Collaborative networks as determinants of knowledge diffusion patterns.

Management Science 51, no. 5:756-770.

30

Singh, Jasjit, and Ajay Agrawal. 2011. Recruiting for ideas: How firms exploit the prior

inventions of new hires. Management Science 57, no. 1:129-150.

Song, Jaeyong, Paul Almeida, and Geraldine Wu. 2003. Learning-by-hiring: When is mobility

more likely to facilitate interfirm knowledge transfer? Management Science 49, no.

4:351-365.

Squicciarini, Mariagrazia, Hélène Dernis, and Chiara Criscuolo. 2013. Measuring patent quality:

Indicators of technological and economic value. OECD Science, Technology and

Industry Working Papers, OECD Publishing.

Sveriges släktforskarförbund. 2011. "Sveriges befolkning 1990." In. Stockholm:

Riksarkivet/SVAR.

Tillväxtverket. Fa-regioner. Tillväxtverket 2005. Available from

http://www.tillvaxtverket.se/huvudmeny/faktaochstatistik/regionalaindelningar/

faregioner.4.21099e4211fdba8c87b800017664.html.

Trajtenberg, Manuel. 1990. A penny for your quotes: Patent citations and the value of

innovations. Rand Journal of Economics 21, no. 1:172-187.

Trajtenberg, Manuel, and Gil Shiff. 2008. Identification and mobility of Israeli patenting

inventors: Pinhas Sapir Center for Development.

Trajtenberg, Manuel, Gil Shiff, and Ran Melamed. 2006. The "names game": Harnessing

inventors' patent data for economic research. Working Paper no. 12479, National Bureau

of Economic Research, Cambridge, MA.

Waldinger, Fabian. 2010. Quality matters: The expulsion of professors and the consequences for

PhD student outcomes in Nazi Germany. Journal of Political Economy 118, no. 4:787-

831.

31

———. 2012. Peer effects in science: Evidence from the dismissal of scientists in Nazi

Germany. Review of Economic Studies 79, no. 2:838-861.

32

Appendix A. Explanations of control variables.

Tenure measures the number of years at the current job, which is linked to experience and on-

the-job training and captures time-varying preferences for job switching. In general, job changes

are less likely when a worker has longer tenure, since tenure implies better matching or when the

worker has invested in job-specific capital (Becker 1975).

Lagged firm patenting is a variable that is included to capture any firm strategy of patenting,

which can be assumed to influence an individual’s propensity to patent positively. We use the

lagged value of this variable in order for it not to be directly influenced by a focal inventor

moving.

Age and Age^2. These age measures control for linear and nonlinear productivity effects that

vary over the life-cycle of inventors.

Bachelor degree or above; Tertiary education. These variables control for patent productivity

that stem from education. For similar reasons, we include dummies for Education type:

medicine; natural sciences; and social sciences, controlling for education field effects that differ

from the baseline category of Education type: technical sciences.

We also include dummies for type of the business group in which the inventor works, again

motivated by the possibility that they may systematically differ in their patenting strategy. We

differentiate between firms belonging to the following categories: a) Swedish, no business group,

b) Swedish business group, c) Swedish business group, foreign subsidiary, and d) Foreign

business group with Swedish subsidiaries. The last category was used as our baseline group in

the regressions.

33

Firm size measures the number of employees at the firm at which the individual works. We

divide this variable in the regressions into the same dummy variable categories as Hoisl (2007),

i.e., in firm sizes of 11-50, 51-250, 251-500, 501-1,500, 1,501-5,000, 5,001-10,000, 10,001-

50,000, and more than 50,000 employees. The base category is 1-10 employees. Larger firms are

more likely to target international markets and may therefore have a more pronounced strategy

toward patent protection, which is not entirely captured by ownership category.

Public sector is a dummy variable included to capture differences in inventiveness resulting from

different incentives for patenting and mobility.

34

Figure 1 Average productivity before and after a move, by order of move.

35

Table I. Descriptive statistics of inventors in year when first observed.

Variable N Mean SD Min Max

Total number of moves by inventor 10481 1.72 1.62 0 12Total patents applied 10481 1.64 0.83 1 4Share male inventors 10481 0.89 0.32 0 1Age 10481 25.08 3.19 21 43Bachelor degree or above 10481 0.26 0.44 0 1Tertiary education 10481 0.41 0.49 0 1Technical education 10481 0.67 0.47 0 1Medicine education 10481 0.03 0.16 0 1Natural science education 10481 0.09 0.28 0 1Social science education 10481 0.03 0.16 0 1Swedish no business group 10481 0.37 0.48 0 1Swedish business group, no foreign subsid. 10481 0.11 0.31 0 1Swedish business group, foreign subsid. 10481 0.42 0.49 0 1Foreign business group, Swedish subsid. 10481 0.09 0.29 0 10-10 employees 10481 0.11 0.31 0 111-50 10481 0.11 0.31 0 151-250 10481 0.13 0.33 0 1251-500 10481 0.06 0.24 0 1501-1500 10481 0.14 0.35 0 11501-5000 10481 0.22 0.41 0 15001-10000 10481 0.11 0.31 0 110001-50000 10481 0.10 0.30 0 1>50000 10481 0.02 0.14 0 1Public sector employee 10481 0.10 0.30 0 1

36

Table II. Distribution of observations (panel A) and individuals (panel B) by cumulative patenting and cumulative mobility.

Panel A. Distribution of observations by cumulative patenting in fractions and cumulative mobility.

Number of moves undertakenPatents 0 1 2 3No patents 54,085 (69) 24,644 (57) 10,994 (48) 4,266 (41)0 < x < 1/4 3,941 (5) 3,046 (7) 1,877 (8) 1,110 (11)1/4 <= x < 1/2 7,908 (10) 6,021 (14) 3,964 (17) 1,997 (19)1/2 <= x < 1 2,333 (3) 1,705 (4) 1,247 (5) 456 (4)1 <= x < 1.5 5,222 (7) 3,908 (9) 2,343 (10) 1,275 (12)1.5 <= x < 2 1,141 (1) 1,182 (3) 623 (3) 377 (4)2 <= x < 5 2,742 (4) 2,219 (5) 1,401 (6) 721 (7)x >= 5 673 (1) 446 (1) 331 (1) 206 (2)Total 78,045 (100) 43,171 (100) 22,780 (100) 10,408 (100)

Panel B. Distribution of individuals by cumulative patenting, fractions just before a move, and by cumulative mobility.

Number of moves undertakenPatents 0 1 2 3No patents 5,702 (82) 3,095 (69) 1,414 (58) 600 (50)0 < x < 1/4 237 (3) 244 (5) 197 (8) 117 (10)1/4 <= x < 1/2 457 (7) 495 (11) 339 (14) 193 (16)1/2 <= x < 1 118 (2) 131 (3) 90 (4) 57 (5)1 <= x < 1.5 259 (4) 261 (6) 214 (9) 116 (10)1.5 <= x < 2 61 (1) 73 (2) 51 (2) 42 (3)2 <= x < 5 117 (2) 152 (3) 101 (4) 69 (6)x >= 5 20 (0) 23 (1) 22 (1) 17 (1)Total 6,971 (100) 4,474 (100) 2,428 (100) 1,211 (100)

Note:The table includes data up to and including move 3, which encompasses 95% of observations.Relative frequencies are given in parentheses.

37

Table III. Correlation between patent productivity, move variables and tenure.

fractions Any move 1st move 2nd move 3rd move 4th move tenure Z1 Z2 Z3 Z4fractions 1

Any move 0.04*** 11st move 0.02*** 0.58*** 12nd move 0.02*** 0.39*** -0.24*** 13rd move 0.02*** 0.25*** -0.16*** -0.11*** 14th move 0.01*** 0.16*** -0.10*** -0.07*** -0.04*** 1tenure 0.07*** -0.12*** -0.02*** -0.06*** -0.06*** -0.05*** 1Z1 -0 0.01** -0 0.01*** -0 -0 -0 1Z2 -0.01*** 0.04*** 0 0.02*** 0.02*** 0.02*** -0.02*** 0.03*** 1Z3 0.01** 0.02*** 0.02*** 0.01* 0*** -0 -0.02*** 0.65*** 0.16*** 1Z4 0.01*** 0.05*** 0.02*** 0.02*** 0.02*** 0.01*** -0.01*** 0.03*** 0.75*** 0.24*** 1Note: Z1 = Employment growth total in sector in region, Z2 = Employment growth total in sector, Z3 = Highly educated employment growth total in sector,

Z4 = Highly educated employment growth in sector in region. *** p < 0.01, ** p < 0.05, * p < 0.1

Table IV. All movers, and first to fourth move effects on patent productivity without controls.

Model A0 A1 A2 A3 A4 Sample All movers First movers Second movers Third movers Fourth moversMove effect (a) 0.0126***

(0315)0.0134***

(0333)-0163(0496)

00317(0628)

-0624(0889)

Year FE YES YES YES YES YESRobust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1

Table V. All moves, moves 1-4 on patent productivity with control variables.

Model: B0 B1 B2 B3 B4

 Sample All movers First movers Second movers Third movers Fourth movers

Move effect 0576 0326 -0208 -00317 -0184

(0407) (0448) (0482) (0666) (0.0100)

Tenure 0213*** 0142** 0248** 00360 0288

(00481) (00566) (00977) (0127) (0219)

Firm lagged pat 00513*** 00439*** 00584*** 00432*** 00249*

(6.57e-05) (7.48e-05) (00100) (00125) (00136)

Age 0.0326*** 0.0355*** 0.0428*** 0.0426*** 0.0400***

(0167) (0211) (0363) (0566) (0816)

Age^2 -00388*** -00417*** -00535*** -00499*** -00463***

(2.24e-05) (2.78e-05) (4.81e-05) (7.70e-05) (00112)

Swe no -0.0264*** -0.0289*** -0.0122** -0594 0.0139

bus. group (0345) (0443) (0575) (0830) (0.0124)

Swedish bus. group, -0.0260*** -0.0311*** -0.0184*** -0785 -0760

no foreign subsid. (0394) (0502) (0636) (0.0102) (0.0130)

Foreign bus. group, -0.0115*** -0.0203*** -0356 -0461 0207

40

Swe subsid. (0363) (0484) (0590) (0741) (0.0114)

11-50 employees -0.0124*** -0.0110* -0.0108 -0152 -0211

(0452) (0619) (0702) (0971) (0.0170)

51-250 -0.0183*** -0.0197*** -0.0112 0821 -0536

(0472) (0616) (0718) (0.0105) (0.0174)

251-500 -0.0145*** -0.0104 -0688 0.0105 0266

(0549) (0718) (0903) (0.0119) (0.0196)

501-1500 -0240 -0570 0102 0.0142 0.0238

(0519) (0696) (0816) (0.0115) (0.0192)

1501-5000 -0415 -0.0110 -0871 0.0272** 0.0419

(0570) (0706) (0904) (0.0134) (0.0255)

5001-10000 -0.0157** -0.0117 -0.0135 -0146 0.0215

(0691) (0822) (0.0136) (0.0143) (0.0217)

10001-50000 -0969 -0982 -0695 -0976 0.0280

(0628) (0811) (0.0110) (0.0171) (0.0226)

>50000 0.0153* 0.0146 0.0408** 0.0225 0.117***

(0887) (0.0111) (0.0159) (0.0369) (0.0431)

Bachelor degree -0980 -0954 -0.0251** -0.0331 0.0373

41

or above (0726) (0908) (0.0126) (0.0239) (0.0436)

Some tertiary -0.0208*** -0.0214*** -0.0242** -0.0274 -0.0188

education (0637) (0788) (0.0118) (0.0182) (0.0334)

Education field: 0870 -00149 0.0498 0.0633 0.311*

medicine (0.0117) (0.0122) (0.0335) (0.0623) (0.174)

Education field: 0.0177 0715 0.0226 0.0770* 0.0731

natural sciences (0.0121) (0.0112) (0.0307) (0.0405) (0.0636)

Education field: -0.0106 -0.0176 0.0220 0.0250 -0.0248

social sciences (0.0108) (0.0140) (0.0162) (0.0360) (0.0272)

Public sector -0.0453*** -0.0473*** -0.0450*** -0.0411*** -0.0333**

(0565) (0687) (0.0143) (0.0127) (0.0152)

Constant -0.541*** -0.589*** -0.700*** -0.761*** -0.795***

(0.0284) (0.0365) (0.0610) (0.0976) (0.153)

Observations 146,328 106,642 64,278 32,568 14,748

Unique inventors 10,408 10,306 7,310 4,511 2,406

Year FE YES YES YES YES YES

R2 0.0237 0.0250 0.0238 0.0104 0443Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1 Omitted categories: Foreign business group, Swedish subsidiary, Firm size 0-10, Technical education.

42

43

Table VI. First to fourth IV move effects on patent productivity.

Model A0 A1 A2 A3 A4 Sample All movers First movers Second movers Third movers Fourth moversFirst stage results

Employment growth 0.0405** 0.0897*** 0.0767***total in sector (0.0182) (0.0181) (0.0284)

Highly educated 0.0299** 0.0829*** 0.0665**employment growth total in sector

(0.0136) (0978) (0.0322)

Employment growth 0320*** 00478*** -0782***in sector in region (0120) (00171) (0266)

Highly educated 0.0106** -0862employment growth in sector in region

(0502) (0572)

Second stage results

Move effect -0.0270 -0.0302 0.0882 -0.0332 -0.266(0.164) (0.130) (0.169) (0.208) (0.451)

Year FE YES YES YES YES YESControls (a) YES YES YES YES YES

44

Table VII. Mobility effects on patent quality.

Sample

First movers Second movers

Third movers Fourth movers

Move effect -.356 (.063)***

-.267(.057)***

-.164(.078)**

-.172(.071)**

Move effect, IV 6.141 (1.794)***

6.002 (2.200)***

11.050(8.610)

4.127(3.729)

Hansen J statistic (p-value)

.050(.823)

1.707(.426)

.025(.873)

.193(.660)

KP F-statistic

Controls YES YES YES YES

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p <0.1First mover instruments: Employment growth in sector in region, Employment growth in sector in totalSecond mover instruments: Employment growth in sector in region, Employment growth in sector in total, Highly educated empl. growth in sector in regionThird mover instruments: Employment growth in sector in region, Highly educated employment growth in sector in totalFourth mover instruments: Employment growth in sector in region, Highly educated employment growth in sector in total

45

1 The description of the dataset construction is taken from Ejermo and Schubert (2014).

2 In contrast to e.g. Danish register data, the exact timing of the move cannot be observed and has to be inferred from

the registers at Statistics Sweden, on a year-to-year basis.

3 Without this variable the move effect coefficient in the “All movers” regression is 0.011 and significant on the 1

percent level.

4 In our previous example, the measure would be two for number of applications and one if one of the two patents were

eventually granted.

5 We also had data on opposition (cf. Harhoff, Scherer, and Vopel 2003), but found no significant result of a move,

probably stemming from the fact that 99.8% of the opposition-weighted observations had a value of zero. Citation-

weighted patents had a bit more variation, with 5% non-zero-value observations.