Growing through Spinoffs. Corporate Governance, Entry, andInnovation

Abstract
New firms are often based on ideas developed within incumbent firms. We study the macroeconomic effects of spinoffs through a growth model of variety expansion, driven by entry, andproduct quality innovation. On the one hand, spinoffs raise aggregate productivity through the
expansion of intermediate products and, if created voluntarily by incumbents, boost their returnto equity. On the other hand, they erode incumbents’ market share and, when stemming fromconflicts between incumbents and their employees, drive up incumbents’ internal cost of capital.
We find that a priori investment protection has an ambiguous impact on spinoff activities. The
calibrated model indicates that a broad legal reform that increases investment protection reduces the spinoff rate but boosts incumbents’ product quality innovation, raising income growth
and welfare. The trade-offs are consistent with firm-level evidence from Italy.
Keywords: Corporate Governance, Endogenous Growth, Spinoffs, Innovation.
JEL Codes: E44, O40, G30

1Introduction

In several countries firms’ spinoffs account for a large share of firm entries, totalling up to 30%
of new firms according to firm-level surveys (see, e.g., Klepper and Sleeper, 2005; Bernardt et al.,2002).1 The history of high-tech companies, indeed, abounds of anecdotes in which ideas developed
within incumbent firms are used as the founding stones of new businesses. For example, many
point at spinoffs as key for the development of the Silicon Valley (Moore and Davis, 2004) andof the rise of Detroit as the capital of the U.S. automobile industry at the onset of the 20th
century (Klepper, 2002; Cabral et al., 2018). In spite of this fundamental role of spinoff activities
in the dynamics of firm creation, the macroeconomics literature appears to lag behind the microoriented literature in investigating their aggregate consequences. Most studies treat firm entry as
1
Klepper and Sleeper (2005) calculate spinoff rates (spinoff entries over total entries) around 20% for various U.S.
industries from the 1960s to the 1990s. Bernardt et al. (2002) report spinoff rates of 5-8% for small Dutch firms
and 40% for large Dutch firms in the 2000s. The estimated spinoff rate is 15% in Sweden for the 1993-2005 period(Andersson and Klepper, 2013), 12.3% in Denmark for 1981-2000 (Eriksson and Kuhn, 2006), and as high as 31% in
Brazil for 1995-2001 (Muendler et al., 2012). See also Hvide (2009) for Norway and Rocha et al. (2015) for Portugal.

1essentially undifferentiated, not distinguishing between firms created ex novo by new entrepreneurs
and spinoffs created from incumbent firms. This can not only distort the conclusions about theaggregate effects of firm entry but also leave policy makers on uncertain grounds. A policy makercould be inclined to point out the social benefits of spinoffs, as these facilitate the disseminationof knowledge and accelerate firm creation. Yet, while many spinoffs are created voluntarily byincumbent firms, a significant share of spinoffs originate from conflicts between incumbent firms
and their employees (Thompson and Klepper, 2005). The policy maker has then to face thepreoccupation of incumbent firms that employees with access to firms’ trade secrets would use
the acquired knowledge to set up rival firms. Furthermore, the literature has pointed out that
infringements of incumbent firms’ proprietary knowledge associated with conflictual spinoffs diluteincumbents’ incentive to invest in the innovation of their own products. Hence, the policy maker’s
aim of favoring knowledge dissemination through spinoffs could have the unintended consequenceof slowing down the economy’s pattern of investments.
The main objective of this paper is to develop an integrated, general equilibrium model offirm entry and knowledge investment that explains how spinoff activities affect income growth
and welfare both in the short and in the long run. We assess quantitatively whether the benefits
associated with the creation of startups that build on knowledge developed within existing firms
are larger than the potential losses due to the dilution of shareholders’ property rights. In themodel, spinoffs can be created by managers on behalf of incumbent firms that seek to expand theirproduct lines (“consensual spinoffs”) or can stem from conflicts of interest between managers andshareholders of incumbents firms (“conflictual spinoffs”; Thompson and Klepper, 2005; Garvin,
1983; Brittain and Freeman, 1986).
To motivate the theoretical and quantitative analysis, we first present evidence for its mechanisms by exploiting rich, firm-level data from the Italian local (provincial) markets. In Italy, as
in several other advanced countries, a large share of firms’ entry occurs through spinoffs - it wasmore than 25% in recent years. Using data spanning the period from the mid-1990s to the early2010s, we find that in Italian provinces with relatively poor judiciary and weak protection of incumbents’ investments, there is a higher spinoff rate (spinoffs to total entries) and a larger incidenceof conflictual spinoffs relative to consensual ones. The evidence also suggests that in provinces2

with poor judiciary, incumbent firms tend to invest less in innovations aimed at improving theirown products. These empirical findings are robust to the inclusion of detailed sets of fixed effects
and carry through when using an administrative reform of Italian courts as an exogenous shock to
the quality of judiciary. The trade-offs highlighted by the empirical findings lie at the core of ourtheoretical and quantitative analysis.
In the model economy, growth is driven both by the foundation of new firms that offer new
intermediate products and by the investment of incumbent firms in the improvement of their own
intermediate products. New firms comprise businesses founded ex novo by households and spinoffs
of incumbent firms. Spinoffs, in turn, can be created by firm managers on behalf of incumbents
that seek to expand their product lines or in conflict with the shareholders of those firms. To model
conflictual spinoffs, we draw from the literature that has extended endogenous growth theory toinclude corporate governance frictions (e.g., Terry, 2017; Iacopetta et al., 2019; Celik and Tian,2018). Households can act as firm managers and shareholders; managers are in charge of executing
investment plans. Managers, however, also develop side projects that consist of setting aside someresources that the firm had earmarked for its own product quality improvement or for the creationof consensual spinoffs in order to start up their own firms (i.e., to engage in conflictual spinoffs).
When created on behalf of incumbent firms, spinoffs can positively affect their return to equity.
Additionally, they have a positive social effect by increasing the productivity of the final good sector
through a wider range of intermediate products. On the other hand, spinoff activities can cause
damages to incumbent companies. One damage occurs in case of conflictual spinoffs and consistsof the loss of diverted resources which would otherwise be used to improve the quality of existingproducts or to expand product lines. This reflects both the direct diversion of resources and thesurplus appropriation associated with managers’ incentivizing contracts.2 . The other damage isdue to a classic business stealing phenomenon: the birth of rival firms takes a share of the market,
eroding the return that parent companies expect from investing in product quality improvement.3
We evaluate the trade-offs and interpret the empirical evidence by calibrating the model withthe technological and preference parameters of an advanced economy and matching the pattern
2

The shareholders of parent companies offer managers incentivizing contracts aimed at reaching the planned
investments (see, e.g., Nikolov and Whited, 2014
3
Clearly, the distortion in product quality investment and the appropriation of surplus by managers also erode
the returns to de novo entry.

3

of spinoff activities to the Italian data. We propose two sets of experiments. In a first set ofexperiments, we compare in the long run economies that differ in investment protection. We focus
on two dimensions of investment protection: the protection of the results of R&D investments in
product quality and of spinoff investments aimed at product variety expansion. We obtain that a
priori an improvement of investment protection has ambiguous effects on spinoff creation and on
incumbent’ investment in product quality. Economies that protect strongly the results of incumbents’ investments in product quality tend to depress spinoff creation and to boost firms’ productquality. Managers, in fact, will be deterred from diverting R&D resources for the creation of conflictual spinoffs; and, as a result, incumbents will have stronger incentives to invest in productquality.4 Investment protection can instead have sharply different consequences when the legal
system is strong in protecting incumbents’ spinoff activities. In such economies, managers will also
be limited in using parent companies’ resources for the creation of their own conflictual start-ups.
Precisely for this reason, however, incumbent firms will invest more in the creation of consensual
spinoffs. Our quantitative analysis reveals that the latter effect dominates over the former, boosting the overall spinoff rate. Even in this dimension of investment protection, however, the mostimportant quantitative effect occurs on the intensive margin. In light of the stronger spinoff protection, incumbents will favor product variety expansion over internal firm growth. Put differently,
a legal system focused on spinoff protection, while boosting spinoff creation, will tend to crowd out
incumbents’ investment in product quality, driving down their internal growth.
The two dimensions of investment protection are inherently intertwined in legal systems. We
then study the impact of a “broad legal reform” that protects both margins of incumbents’ investment, such as a general improvement in legal protection. Our quantitative analysis indicates thatsuch a broad improvement of investment protection results in overall lower spinoff creation but ina more intense R&D investment in product quality, ultimately leading to higher income growth.
Quantitatively, an economy with half the R&D and spinoff diversion compared to the baseline
economy is characterized by a spinoff rate that is 0.72% lower but a firm knowledge rate of growtharound one-tenth faster. The empirical estimates for the effect of Italian courts efficiency suggest
a similar trade-off.
4

Although the base from which managers subtract resources is larger, overall there are fewer spinoffs.

4

In a subsequent series of experiments, we employ the model’s dynamics to analyze the shortand long-term effects of such a broad legal reform that enhances investment protection for both
R&D and spinoff activities. In the short run, there is a decrease of the spinoff rate and of theoverall rate of firm entry. Conversely, the product quality of incumbents improves at a quicker
pace. The reduction in firm entry progressively leads to an expansion of incumbents’ size, further
motivating them to invest in product quality. Upon the implementation of a reform that reduces
investment diversion by half (from a baseline of 4%), our analysis reveals a decrease in the spinoff
rate comparable to the long-term decline (0.72%). On the other hand, there is an increase in theinvestment in product quality by 0.017%, which constitutes approximately three-quarters of thelong-term increase. The remaining quarter is due to the transition to the new long-term equilibrium.
In line with the “rule of law view”, we find that, following such a broad legal reform, welfare improves
both in the short and in the long run. Interestingly, along the transition, the welfare improvement
induced by incumbents’ higher investment in product quality would be outweighed by the welfare
loss due to the lower spinoff creation. However, de novo entry intensifies thanks to incumbents’higher investment profitability and this partly offsets the welfare damage of lower spinoff creation.
To get a better sense of the quantitative effects of investment protection policies, in the last partof the paper we compare them with those of subsidy policies (pairwise comparisons are designed tohave the same long-run effect on per capita output growth). In the short and medium run, a change
in R&D protection that loosens up the barriers to conflictual spinoffs is about one-third as effective
as an entry subsidy in promoting firm entry while it reduces incumbents’ growth and overall welfare
significantly more than an entry subsidy. On the other hand, an improvement of R&D protection
produces similar effects on entry, incumbents’ growth and welfare as an R&D subsidy.
The paper unfolds as follows. Section 2 relates the analysis to the literature. Section 3 presents
the empirical evidence. Section 4 lays out the model and Section 5 solves for the equilibrium.
Section 6 examines the steady state and the dynamics. Sections 7 and 8 analyze the effects quantitatively. Section 9 concludes. Proofs and details on the data are relegated to the Online Appendix.

5

2

Prior Literature

The paper relates to two main strands of literature. A first strand investigates the role of firm entry,
and its interaction with incumbents’ investment decisions, in driving growth (see, e.g., Peretto, 1999;
Young, 1998; Howitt, 1999; and, for a survey, Etro, 2009). This literature does not focus on the
distinction between firm de novo entries and corporate spinoffs. This is perhaps due to the limitedemphasis of the growth literature on the corporate governance issues surrounding the creationof spinoffs.5 A second related strand of literature has recently started to incorporate corporate
governance frictions into endogenous growth models. Studies in this strand comprise Akcigit etal. (2021), Caselli and Gennaioli (2013), Cooley et al. (2014), Terry (2017), and Iacopetta etal. (2019). In Akcigit et al. (2021) conflicts between managers and shareholders affect firms’
investment decisions and limit the growth of product variety because of a span of control problem.
In Iacopetta et al. (2019) conflicts between managers and shareholders influence the returns to denovo firm entry and to incumbents’ investment. In their setting, de novo entry and incumbents’investment interact with each other through the structure of the industry. These papers do not
focus on the role of spinoff entry in innovation and growth.6
Besides the analysis of the aggregate impact of spinoffs, another element that distinguishes ourpaper from previous works on the macroeconomic effects of corporate governance is that the conflicts of interest between managers and shareholders are not modelled as managers’ unproductive
objectives (e.g., perks, tunneling, or a “quite life”). In fact, in our economy managers employ
diverted resources to establish new firms in a more efficient way than de novo entrepreneurs. This
insight builds on a typical theme of the micro-oriented literature that spinoffs are more common
when workers feel that parent companies resist the development of new ideas and products (Hellmann and Thiele, 2011; Garvin, 1983; Brittain and Freeman, 1986). Chatterjee and Rossi-Hansberg(2012) investigate managers’ decision to create spinoffs or sell innovative ideas to incumbent firms
in an industry where managers have private information on the quality of their ideas. Thompson
and Klepper (2005) observe that managers who spawned new firms often had conflicting views withparent companies about the development of new projects. Franco and Filson (2006), Baslandze
5
6

For a discussion of the literature on the growth effects of knowledge spillovers see, e.g., Aghion and Howitt (1998).
For studies on financial markets and growth see, e.g., Aghion et al. (2005) and Cooley and Quadrini (2001).

6

(2022), and Sohail (2021) study how managers’ decision to leave incumbent firms and create spinoffschanges as the distribution of know-how in the industry evolves. While these works focus on how
firm size, in terms of employment or knowledge capital, conditions the creation of spinoffs, our workemphasizes the role of corporate governance in accounting for spinoffs. In line with these studies, inour economy managers can divert resources earmarked to product improvement or to the creationof new product lines with the intent to develop new products in their own new enterprises (conflictual spinoffs). In doing so, managers exploit knowledge they acquired while working for incumbent
firms. Thus, spinoffs contribute to variety expansion and do not replace parent companies which
continue to operate existing product lines. Christensen (1993), for instance, finds that spinoffs in
the disk drive industry developed new, smaller disk drives relative to those produced by parent
companies.7 In this dimension, our paper also broadly relates to a strand of studies that contrast
the benefits of the patent protection of inventors against the hurdle that such protection poses tothe diffusion of ideas (see, e.g., Boldrin and Levine, 2013, and Stiglitz, 2014).
Relative to prior studies on the microeconomic determinants of spinoffs and on their impact
on the industry equilibrium, the main contribution of our work is that it investigates the macroeconomic implications of spinoff activities for the investment, entry and growth dynamics of theeconomy in a unified, general equilibrium framework. This proves to be useful when carryingout a welfare analysis. As noted, while some argue that spinoffs are beneficial because they spur
new technologies that might have stayed dormant in parent companies, others are concerned thatconflictual spinoffs infringe the investors’ rights of parent companies by taking away valuable resources. Our analysis allows to investigate this trade-off offering insights into the policies that canbetter promote growth and welfare. Although Baslandze (2022) and our work both aim to quantify
the aggregate productivity effects of spinoffs through the lens of endogenous growth models, theydiffer in the underlying mechanisms and model. In our framework, the key drivers of growth areincumbent firms’ investment in quality and horizontal expansion, whereas in Baslandze (2022) the
main source of growth is creative destruction. Baslandze (2022) emphasizes the role of incumbents’heterogeneity, whereas we collapse the distribution of firms to have an analytical solution for theequilibrium dynamics.
7
Samaniego and Sun (2019) develop a model where a weak rule of law leads to the proliferation of substandard
intermediate goods by incumbent firms.

7

3

Empirical Evidence

The model presented in the next section will show that the protection of investors conditions
the number of consensual and conflictual spinoffs from incumbent firms and the propensity ofincumbents to spend in in-house investments. This section shows that these key relationships
emerge from an empirical analysis on Italian data. Italy is an ideal testing ground to investigate
the role of investment protection in the creation of new firms and in the incidence of spinoffs. While
the same laws apply across the Italian territory, judicial efficiency exhibits marked heterogeneity
across Italian provinces (World Bank, 2013; Carmignani and Giacomelli, 2009).8

3.1

Innovation, spinoff activities, and investment protection in Italy

It is useful to first review the patterns of spinoff activities, firms’ innovation, and judicial efficiency
in the Italian context. For this overview, we rely on recent cross-sectional data compiled by the
Italian National Institute of Statistics (for incumbents’ innovation and judicial efficiency) and by
the Italian Ministry for Economic Development (for firms’ spinoff activities).
Appendix Figure (A1), Panel A, displays the spinoff rate (ratio of spinoffs to total firm entries)
in the Italian local (provincial) markets in 2015. The data are drawn from the “Rilevazione sul
Sistema delle Start-up Innovative”, a survey of start-ups carried out by the Italian Ministry of
Economic Development. In line with prior literature, spinoffs are defined as newly created firms
whose founders were previously full-time managers or employees of incumbent firms operating inthe sector (see, e.g., Andersson and Klepper, 2013, for a similar definition). This comprises bothinstances in which managers carried out spinoffs on behalf of incumbent firms and instances ofconflictual spinoff creation. Figure (A1), Panel B, displays the proportion of innovative firms in
the Italian provinces, sourced from the “Survey of Italian Manufacturing Firms” conducted by the
Italian banking group UniCredit. Further, Figure (A1), Panel C, shows the number of researchand development (R&D) workers, normalized by the population size, using data from the survey ofinnovative firms conducted by the Italian National Institute of Statistics (ISTAT).9 Overall, Figure(A1) yields two main insights. First, spinoffs constitute a large share of new firm entries in Italian89Italian provinces are geographical entities similar in size to U.S. counties.
ISTAT data on R&D workers are at the regional level. An Italian region comprises one or multiple provinces.

8

local markets, accounting for about 28% of firm entries on average and for more than 35% of entries
in several provinces. Second, both the incidence of spinoffs and the incidence of innovative firms(including their R&D activities) vary greatly across local markets.
Drawing data from ISTAT, Figure (A1), Panel D, displays indicators of judicial efficiency in the
Italian provincial markets. As noted, there is significant heterogeneity of judicial efficiency acrossprovinces. For example, the cost of bankruptcy court procedures as a share of firms’ assets goesfrom 2.4% for the courts of the northern province of Trieste to 33.9% for the courts of the southern
province of Lecce. And heterogeneity exists even across provinces within the same geographicalarea. For instance, the cost of court procedures in the provinces of Brindisi and Taranto - both
located in the South - are equal to 3.7% and 29.3% of firms’ assets, respectively.

3.2

Data and measurement

To conduct our empirical analysis, we exploit detailed data from the Italian business sector from1995 to the late 2000s (with the exact terminal year of the sample depending on specifications anddata availability). The analysis combines data from three main sources: the MET survey of Italianbusinesses, which is the largest firm survey conducted in a single European country; the “Survey of
Italian Manufacturing Firms”carried out by the Italian banking group UniCredit; data of ISTAT
on judicial efficiency at the provincial level. We complement these databases with other sources,
including ISTAT data on institutional and economic characteristics of the Italian provinces, Bankof Italy data on the structure of provincial banking sectors, and data of the Institute Guglielmo
Tagliacarne on provincial infrastructures. More details on the data are in the Appendix.
In studying the incidence of spinoffs among firm entries, we treat the Italian provinces as ourgeographical unit of observation. Information on the share of spinoffs in a given province and period
is drawn from the MET survey, which covers about 25,000 firms in each of its waves. The MET
survey is ideally suited for this purpose as it asks each firm a precise question regarding the personal
background of its founders, namely whether they were previously full-time managers or employees ofincumbent firms operating in the sector. Exploiting this question, for each province and three-yeartime period from 1995 to 2009 (hence, for a total of 5 time windows), we compute the share of newly
created firms whose founders were previously full-time employees of a firm operating in the same9

sector, relative to the total number of newly created firms. Observe that, as stressed by the survey
questionnaire, the MET information captures all types of spinoffs, conflictual or consensual; we will
return later to the task of distinguishing between the two types of spinoffs. Firm-level informationon the investments in innovation of incumbent firms is drawn from the Capitalia Survey of Italian
Manufacturing Firms. This survey targets the universe of publicly listed companies and a large
representative sample of non-listed firms with no less than 10 employees. This threshold numberof employees implies that the survey naturally focuses on incumbent businesses: the mean age ofthe firms is 22 years, and the first age decile is 6 years. To proxy for in-house innovation we use adummy that takes the value of one if a firm declares that during the period it introduced any typeof innovation aimed at the improvement of its products, zero otherwise. In addition to this binary
indicator, we also consider the firm’s ratio of R&D expenditures over sales. The information on
firms’ innovation activities is available for four three-year time windows, from 1995 to 2006.
To measure investment protection in the local (provincial) markets, we consider widely used
proxies for judicial (court) efficiency in the provinces. Using ISTAT data, as an inverse measure ofcourt efficiency, we consider the average duration of trials (in days) in the courts of the province.
As an alternative measure, we also use the clearance rate of the courts in the province, defined as
the ratio of trials concluded during the time period over the flow of new trials.
In the regressions, we control for a broad array of possible determinants of firm entry and of firm
innovation (more details below). In addition, by exploiting the cross-sectional and time variationof our data, we include both time and geographical area fixed effects.

3.3

Empirical models

To study the effect of legal protection on spinoff creation, we estimate the following empirical modelat the province level:
Spinof fjt = α + βCourtjt + γ ⊤ Xjt + ηj + ζt + εjt .

(1)

In equation (1), Spinof fjt is the spinoff rate (ratio of spinoffs over total newly created firms) inprovince j during a (three-year) period t; Courtjt is the measure of court efficiency in province j and10period t; Xjt is a comprehensive vector of province-level time-varying controls, including economic
conditions (unemployment rate), population growth, banking development and concentration, trade
openness, and material infrastructures; ηj and ζt are respectively geographical area and time (threeyear period) fixed effects; and εjt is the error term. The maximum sample on which we estimate
the model in (1) consists of 515 province-time observations over the 1995-2009 period.10
To investigate the impact of legal protection on incumbents’ innovation in product quality, weinstead estimate the following empirical model at the firm level:
Innovijt = α′ + β ′ Courtjt + γ ′⊤ Xjt + δ ′⊤ Zit + ηj′ + ζt′ + ε′ijt .

(2)

In equation (2), Innovijt is a dummy that takes the value of one if firm i in province j introduced
innovations aimed at product improvement in period t; Courtjt and Xjt are respectively the measureof court efficiency and the vector of province-level time-varying controls also included in equation(1); Zit is a vector of firm-level time-varying controls comprising the firm’s age, size and profitability;
ηj and ζt are respectively geographical area and time fixed effects; and εijt is the error term. The
maximum sample on which we estimate the model in (2) consists of 17,000 firm observations for
1995-2006.

3.4

Estimates

In Table 1, Panel A, we present the coefficient estimates for the effect of local court efficiency on
the spinoff rate in a province and time period.11 Regardless of whether we use the duration of court
trials as an inverse measure of court efficiency or the clearance rate as a direct measure of court
efficiency, we find that in provinces where courts are more efficient the incidence of spinoffs in firm
entry is lower. The estimated coefficient βb suggests that a 10 percent decrease in the duration ofprovincial court trials is associated with a 0.4 percentage point decrease in the spinoff rate in theprovince (about 1.5 percent of the average spinoff rate).
In Panel B of Table 1, we examine the impact of judicial efficiency on incumbent firms’ inhouse innovation. The regressions show a positive association between judicial efficiency and the10This sample size refers to specifications using the courts’ clearance rate, while we experience the loss of someobservations when using the duration of trials.
11
In all the regressions, standard errors are heteroskedasticity robust and clustered at the provincial level.

11

innovation activities of incumbents, whether this is measured by a binary innovation outcome orby the R&D to sales ratio of the firms. The estimated coefficient βb′ in column 5 suggests that a 10
percent decrease in the duration of court trials is associated with a 5.8 percentage point increasein the probability that a firm innovates (about 0.8 percent of the mean probability of innovation).
And column 6 suggests that a 10 percent decrease in the duration of court trials is associated with
a 0.4 percent increase in the R&D to sales ratio. Overall, the estimates thus suggest that higher
court efficiency is associated with significantly lower spinoff rates and higher in-house innovation
rates.
In spite of the inclusion of granular fixed effects and control variables, the reader might still beconcerned about possible endogeneity. For example, omitted variables could be correlated with thequality of local courts and also affect firm creation decisions. To mitigate this concern, in columns
3-4 of Panel A, we exploit a quasi-natural experiment associated with an exogenous change in the
structural organization of Italian courts. A major reform of the court system occurred in 2012,
becoming effective in September 2013. This reorganization led to the suppression of 26 of the 165
pre-existing courts. The district of each suppressed court was merged with an adjacent district ofa surviving court. Such “court mergers” plausibly induced a heterogeneous shock to the quality of
judicial enforcement in the provinces (Pezone, 2021). Notably, such reorganization was not madeon a case-by-case basis, but was governed by ex-ante, mechanical criteria. All courts not located inprovincial capitals had to be suppressed, under the constraint of keeping at least three courts foreach appeals court. Importantly, courts’ productivity or trends in productivity were not criteria
used for deciding the courts to be suppressed. As a robustness check, we then exploit the exogenous
shock induced by this court reform. For this robustness exercise, we focus on the analysis of spinoff
creation, for which we also have data for the period surrounding the court reform (while in-houseinnovation data are not available for that period). In column 3 of Panel A we regress the provincial
spinoff rate in 2014 on the difference between the average trial duration in the province post-reform(2014) and before the reform (2012). In column 4, we repeat the exercise by considering thedifference in the clearance rate. Consistent with the baseline results, we find that an increase in
judicial efficiency (presumably due to the reform) reduced the spinoff rate in the province.

12

3.5

Conflictual and consensual spinoffs

Thus far, we have not distinguished between spinoffs driven by conflicts between firm shareholders
and managers and spinoffs that are voluntarily created by incumbent firms. The Capitalia Survey
asks each firm a question whether in the past three years the firm deliberately spun off productionlines externally to the firm, creating new business entities. Based on this question, we constructa proxy for voluntary spinoff creations on the part of incumbent firms: a dummy that takes thevalue of one if a firm created spinoffs voluntarily. Panel C of Table 1, columns 9-10, shows thatthe negative effect of court efficiency detected for the overall spinoff rate in Panel A does not carrythrough when using this (firm-level) proxy for voluntary spinoffs as the dependent variable. This is
in line with the idea that voluntary spinoffs are not created in conflict with incumbent firms and,hence, should not be deterred by investment legal protection.
We then turn to construct a proxy of the ratio between consensual and total spinoffs in a province
and time period. To this end, we combine information on the percentage of incumbent firms thatcreate consensual spinoffs with the previously used data on the ratio of total spinoffs over entrants,
as well as with province-by-time data on the ratio of entrants to incumbents. The constructedproxy suggests than on average, over the 1995-2006 period, consensual spinoffs represented about
76 percent of the total spinoffs occurred in a province. In columns 11-12 of Panel C, we re-run
the regression in equation (1) using this proxy as the dependent variable. We observe that an
improvement of judicial efficiency increases significantly the share of consensual spinoffs. Overall
our results thus suggest that stronger investment protection reduces the total spinoff rate and raises
the importance of consensual spinoffs relative to conflictual spinoffs.

4

The Model

We build on a large literature that sees delegation as a strategy to achieve better outcomes byassigning decision rights to better informed or more able parties ( see Aghion et al., 2014 for areview). In our setting, firms’ owners (shareholders) can delegate control over investment decisions
to managers, who are more skilled than owners in operating firms. Delegation of control, however,generates a divergence of interests between owners and managers. Our paper follows the “rent13extraction” approach of corporate finance and models the divergence of interests between managers
and shareholders as diversion of firms’ resources (see, e.g., Iacopetta et al., 2019; Iacopetta andPeretto, 2021; and Edmans and Gabaix, 2016 for a review). In our setting, managers subtract
resources earmarked by incumbent firms to product quality improvement or product line expansion(consensual spinoffs) and use them to set up new firms, that is, to spinoff new entities of which
they are the owners (conflictual spinoffs). These rival with incumbent firms in product markets.
The model economy has the following structure. Time is continuous and runs up to infinity. Allvariables are functions of time but we omit the time argument unless necessary to avoid confusion.
The mass of households, L, expands at a constant rate λ. Each household supplies inelastically one
unit of time to a final good sector. In addition to supplying labor, households can act as shareholders(founders) and managers of intermediate goods firms. The final good sector combines labor anda variety N of non-durable intermediate goods to produce an homogeneous consumption good.
Intermediate goods are produced from the final good. In what follows, we detail the technologyof the final and intermediate goods sectors, and the decisions of shareholders, managers, andhouseholds about investments, entry, and consumption.

4.1

Final producers

A competitive representative firm produces a final good that can be consumed, used to produce
intermediate goods, invested in the improvement of the quality of existing intermediate goods, orinvested in the creation of new intermediate goods. The final good is our numeraire. The final
good production technology is
Z N
Y =
0

Xiθ


Qi

L
N 1−σ

1−θ
di,

0 < θ < 1, 0 ≤ σ < 1,(3)

where Xi is the amount of intermediate good i, N is the mass of non-durable intermediate goods,
and L is the flow of labor services. The quality, Qi , of intermediate good i is the good’s ability
to raise the productivity of the other production factors. The parameter θ is the elasticity ofoutput to intermediates; σ governs the love for variety. This raises the marginal productivity of allintermediates. The congestion exponent 1 − σ makes such effect less pronounced than in Romer

14

(1990) types of models. Because in our set up spinoffs fuel the entry of new firms, they contributeto aggregate productivity gains through the variety effect.
Let Pi be the price of intermediate good i and w be the wage. The profit maximization problem
yields that the final producer demands intermediate goods and labor according to:

Xi =

θ
Pi

 1

1−θ

Qi

L
N 1−σ

,

w = (1 − θ)

Y
.
L

(4)

Therefore, the final producer pays
Z N
Pi Xi di = θY,

wL = (1 − θ) Y,(5)0to, respectively, suppliers of intermediate goods and labor.

4.2

Intermediate good producers

Each intermediate firm i has to take decisions about production and investment. Investment canbe of two types. One is the accumulation of knowledge, which enhances the quality of the firm’s
intermediate product, Qi . Enhancing the product quality earns the firm a larger market (see (4)).
A second type of investment is the expansion of product lines through the creation of spinoff firms.
We refer to these two investments as R&D and spinoff investments, respectively. The founder of thefirm could be in charge of executing investments. He may, however, delegate one or both investment
activities to professional managers who are more efficient in executing investments. Delegation isnevertheless costly, because managers seek to earn a private benefit by diverting resources fromthe firm. A broad body of studies (e.g., Klepper and Sleeper, 2005; Thompson and Klepper,
2005; Hellmann and Thiele, 2011; Garvin, 1983; Brittain and Freeman, 1986) show that employees
can devote effort, ideas and R&D findings (“investment resources”) to plan and set up their own
businesses (engage in conflictual spinoffs) rather than to increase the value of parent firms.12
We present the production and investment decisions of an intermediate firm whose founder
12

Given our focus on the aggregate effects of spinoffs, we specify the managerial sector in a simplified way. We do
not model a managerial market, or hiring and firing processes of managers. Thus, shareholders partially discipline
managers through incentivizing contracts, rather than through firing threats. On the other hand, since managers arehomogeneous and behave the same way, a firm would have no incentive to fire a manager and hire a new one.

15

delegates both R&D and spinoff activities. We then study the conditions for delegation to takeplace. For clarity of exposition, in an intermediate firm R&D and spinoff investments are executedby different agents to whom we refer as “R&D manager”and “spinoff manager”, respectively. We
begin with a description of the production and investment processes and then describe firms’ entry.
Production and product quality. Intermediate firm i can transform one unit of final good
into one unit of its intermediate good. Production also entails a fixed operating cost ϕQi in unitsof the final good, where Qi is the quality of the intermediate good i. The firm’s rise in the stock ofknowledge Zi improves the quality of its intermediate product. This is given by
Qi = Ziα Z 1−α ,(6)where 0 < α < 1. Thus, the contribution of good i to factor productivity downstream depends on
RN
the knowledge of firm i, Zi , and on the average knowledge of the industry Z = 0 (Zi /N ) di.
In-house innovation in product quality.

Under R&D delegation, the firm’s stock ofknowledge, Zi , increases according toŻi = h(1 − SIi ) · Ii ,(7)

where h > 1 is the R&D manager’s efficiency (this is equal to 1 for the founder) in running the
R&D lab, Ii denotes the investment in units of the final good that the founder allocates to in-house
knowledge accumulation (R&D) for the improvement of its product quality, and SIi is the fractionof R&D investment funds diverted by the R&D manager.
Entry. We distinguish between ex-novo and spinoff entry. Any intermediate firm, regardlessof how it is created, starts operations with an industry-average stock of knowledge Z. However,thanks to the experience accumulated by managers in incumbent firms, the cost of entry of a spinoff
created by a professional manager is lower that that of an ex-novo firm.
Ex-Novo entry. An ex-novo firm is financed through households’ savings. A de-novo firm starts
operations with an average-industry knowledge stock Z, as noted. Entry requires payment of asunk entry cost βX in units of the final good, where X is the industry average of firms’ output and16

is meant to capture the targeted volume of production of the entering firm. Under free entry, thevalue of a new firm, Vi , is equal to its entry cost:

Vi = βX.

Consensual spinoffs. A spinoff carried out by a manager can use one unit of final good of thefirm to cover e > 1 units of entry cost. The assumption that e is larger than one captures the idea
that, relative to households, managers are better positioned to start up a new firm thanks to theirexperience matured in incumbent firms. Thus, the parameter e measures how knowledge transfers
can defray entry costs. When delegating the creation of spinoffs to a manager, an incumbent firm
gives to the manager access to its resources for spinoff creation. Formally, denoting with Fi thefirm’s flow of cash, a spinoff manager who uses a fraction ai of such available resources creates onbehalf of the incumbent firm the flow of consensual spinoff firms

ṄFi =e(1 − SJi )ai Fi
,
βX

(8)

where the variable 0 < SJi < 1 is the fraction of resources diverted by the spinoff manager for his
own private benefits (i.e., to create conflictually his own spinoff firms).
Conflictual spinoffs. Either type of manager, while under the payroll of the firm, can use
diverted resources as seeds to develop a parallel startup project. Specifically, with every unit ofsubtracted resources a manager can cover e > 1 units of setup cost of a new firm (a conflictual
spinoff). Although managers have the option of immediately consuming the diverted resources,
this is less desirable than investing the resources in firm creation because e > 1. Hence, with theresources diverted from firm i the R&D manager creates a flow of conflictual spinoffs ofeSIi Ii,
βX(9)eSJi ai Fi
.
βX

(10)

ṄIi =and the spinoff manager
ṄJi =All intermediate firms, regardless of their creation process, operate under Bertrand competition.
17

The presence of entry sunk costs then implies that an entrant introduces a new good rather thancompeting with an existing producer. Therefore, in our set up there is only one firm per productline. Each firm can shift its demand curve to the right by investing in the quality of the goodit sells. This boosts the firm’s profitability since the demand system in (4) yields that the firm
charges a constant markup over the marginal cost of production.

4.2.1

The R&D manager

The founder earmarks a flow of investment Ii to knowledge accumulation, Zi . To mitigate diversion,
the founder offers the R&D manager an incentivizing contract ωIi Żi , which is conditioned on the
results of R&D investments (i.e., the increase in the firm’s knowledge stock). In addition to thiscontractual compensation, the R&D manager uses diverted resources to create firms worth V ṄIi ,where V is the expected value of a newly created firm. The diversion effort cost is proportional
to the flow of investments: δI cI (SIi )Ii , with c′I (SIi ) > 0 and c′′I (SIi ) > 0. The parameter δI andthe curvature of cI (SIi ) capture institutional features of the economy, that is, the extent to which
the legal system is able to mitigate managers’ tendency to divert firms’ R&D resources. Thus, theR&D manager, taking as given the founder’s choices of Ii and ωi , maximizes the flow of utility

uIi = ωIi h(1 − SIi )Ii + eSIi Ii − δI cI (SIi )Ii ,(11)

where we used (7) and (9) to replace Żi and ṄIi , as well as the free-entry condition Vi = βX. The
diversion rate chosen by the R&D manager satisfies the marginal conditione = δI c′I (SIi ) + hωIi .

(12)

The condition says that the diversion costs comprise not only the manager’s diversion effort but
also the forgone contractual compensations ωIi . We denote with SIi = SI (ωIi ; δI , h, e) the solutionof (12). The R&D manager participates to the contract if his discounted flow of utility is positive,

ViR&D =Z ∞Rτe− t r(s)ds uIi dτ > 0,

t

18

(13)

where uIi is evaluated on the reaction function (12).
4.2.2

The spinoff manager

The activity of the spinoff manager is one of innovation as much as that of the R&D manager, exceptthat it fuels horizontal rather than vertical expansion. The spinoff manager receives a compensation
proportional to the value of created consensual spinoffs: ωJi ṄFi V . He is also able to divert resources
for SJi ṄJi from the funds available for spinoff activities. The manager sustains an effort cost, bothto spinoff firms on behalf of the parent company and to divert resources for conflictual spinoffs. To
keep the analysis simple the cost function is separable in the two types of effort. Specifically, thecost of creating spinoffs on behalf of the parent company is proportional to the funds earmarked
to spinoffs, ai Fi , that is δ̂J ĉJ (ai )Fi , where δ̂J > 0 and ĉ′J (ai ) > 0, ĉ′′J (ai ) > 0. This assumption
implies that the marginal cost of spinoffs increases as the manager employs a greater share of cash
for such activities. Similarly, the cost of diverting resources for conflictual spinoffs is δJ cJ (SJi )ai Fi ,with δJ > 0, c′J (SJi ) > 0, c′′J (SJi ) > 0. Then, the spinoff manager maximizes
uJi = ωJi e(1 − SJi )ai Fi + eSJi ai Fi − (δJ cJ (SJi )ai + δ̂J ĉJ (ai ))Fi ,(14)

where we used (8) and (10) to replace ṄFi and ṄJi , as well as the free entry condition Vi = βX.
The diversion rate chosen by the spinoff manager satisfies the conditione = δJ c′J + eωJi .

(15)

This says that the gains of diversion equal the marginal cost of effort plus the loss of some compensation. The condition for the usage of funds for spinoff activities (i.e., the choice of ai ) isωJi e(1 − SJi ) + eSJi = δ̂J ĉ′J .

(16)

In this case, the marginal gains are the contractual shares of the value of consensual spinoffs plus
the non-contractual capital gains associated with the diversion of spinoff resources. We denotethe joint solution of (15) and (16) with SJi = SJ (ωJi ; δJ , e) and ai = a(ωJi ; δ̂J , e). The spinoff

19

manager’s participation constraint requires

ViSO =Z ∞Rτ

e− t r(s)ds uJi dτ > 0,

(17)

t

where uJi is evaluated on the optimal conditions (15)-(16).
4.2.3

The founder (principal)

We now consider the dynamic problem of a firm from the founder’s point of view. It is useful firstto define the earnings of the founder in a basic scenario in which the founder retains full controlof the firm. In such scenario there is no creation of spinoffs (the founder has no entry cost benefit
and would have to sustain a spinoff creation cost). Moreover, the firm’s stock of knowledge wouldgrow according to Żi = Ii and the founder would earn a flow of cash of Fi = Πi − Ii , whereΠi = (Pi − 1)Xi − ϕQi ,(18)

is the firm’s profits, with Pi being the price the firm charges for one unit of product of quality Qi .
In such scenario Fi corresponds to the dividend income received by the founder.
When the founder delegates R&D to a manager, the stock of knowledge expands according to(7), that is, Żi = h(1 − SIi )Ii and the flow of cash becomes

Fi = Πi − Ii − ωIi h(1 − SIi )Ii .

(19)

When the founder also hires a spinoff manager with the task of creating new product lines (consensual spinoffs), his flow of cash reduces to (1 − ai )Fi − ωJi e(1 − SJi )ai Fi but he has capital gains
for e(1 − SJi )ai Fi . Thus, the sum of the founder’s flow of cash and wealth creation isΓi = Σi [Πi − Ii − ωIi h(1 − SIi )Ii ],(20)

Σi ≡ (1 − ai ) + (e − ωJi )ai (1 − SJi ) > 1

(21)

where

20

measures the spinoff surplus that the founder can extract from delegating spinoff creation to a
manager (without such delegation Σi would be replaced by one in (20)).
The founder chooses the price Pi , a set of contracts for the managers (ωIi , ωJi ), and a plan ofinvestment Ii , with the objective to maximizeZ ∞Vi (t) =Rτ

e− t r(s)ds Γi (τ ) dτ,

(22)

t

subject to the demand schedule in (4), the R&D investment technology (7), the managers’ best
responses (12), (15) and (16), and the managers’ participation constraints (13) and (17). The
optimal solution (see the Appendix for details) yields a time-invariant monopolistic price Pi = 1θ .
The optimal contractual term ωJi for the spinoff manager satisfies(e − ωJi )ai (−∂a∂SJ) + [(e − ωJi )(1 − SJi )]= (1 − SJi )ai ,
∂ωJi
∂ωJi

(23)

which says that the marginal benefits of ωJi , given by the diminished diversion and the capital gains
for the founder (left-hand side), must be equal to the marginal cost of incentivizing the spinoff
manager. Through the compensation ωJi the founder can affect the spinoff manager’s reaction
functions (15) and (16), influencing both the manager’s use of resources for consensual spinoffs andhis diversion effort for conflictual spinoffs. The joint solution of (15), (16), and (23) delivers thefirm-invariant and time-invariant values SJi = SJ (δJ , e), ωJi = ωJ (δJ , e) and ai = a(δJ , e).
The optimal condition for the compensation of the R&D manager, ωIi , in turn satisfies(1 − SIi )2 h = Σi (−∂SI (ωIi ; δI, h, e)
).
∂ωIi

(24)

This condition states that the founder’s marginal cost of incentivizing the R&D manager, 1 − SIi ,is equal to the marginal gain generated by the reduction of diversion,∂SI (ωIi ;δI, h,e).
∂ωIi

In brief, the joint solution of (12), (15), (16), (23), and (24) yields the optimal compensations,and the optimal diversion and spinoff efforts: SJi = SJ (δJ , e), ωJi = ωJ (δJ , e), ai = a(δJ , e), SIi =SI (δI , δJ , h, e) and ωIi = ωI (δI , δJ , h, e). These variables are firm and time invariant. To ease theexposition, from now on we will drop the index i from the governance variables and omit to indicate

21their dependence on the parameters δI , δJ , h, and e unless necessary to recall such relationships.
Return to in-house investment in product quality. The rate of return to in-house innovation isr = rZi ≡

α Πi
q Zi

(25)

where q is the shadow value of knowledge. The optimal condition on the investment flow, Ii ,requires q to be equal to the (founder’s) cost of knowledge accumulation:q=Σ+ ωI .
h(1 − SI )(26)

In a set up with no spinoffs and no managers q = 1, as one unit of final good would be converted
into one unit of firm knowledge. A higher spinoffs surplus Σ increases the opportunity cost of
in-house investment. Such cost rises also when the R&D agency issues, captured by ωI and SI ,increase. Conversely, the R&D manager’s ability, h, lowers q and thus increases the return rZ .
The arbitrage condition (25) also says that when the spinoff agency cost increases (SJ or ωJ
are higher), the founder needs to accumulate more knowledge Zi to match the outside return r.
iThis follows from the property that Π
Zi is decreasing in Zi . Intuitively, when agency issues are more

severe on the spinoff side than on R&D, shareholders tend to invest relatively more in firm growth.
Return to equity. Time derivation of (22) yields the households’ return on de-novo entry,r = ΓVii + VV̇i . Combining this with the free-entry condition, Vi = βX, and using the definition of Γiin (20), we obtain (taking logs and time derivatives)r = rEi ≡Σ[Πi − Ii − ωI h(1 − SI )Ii ] Ẋ
+ .
βX
X

(27)

Both spinoff and R&D agency issues lower households’ return to equity. In particular, (27) showsthat a higher surplus from spinoff activities Σ boosts firms’ return to equity, incentivizing entry.

4.2.4

Delegation

The analysis focuses on a solution whereby the founder delegates to managers both R&D activities
and the creation of new product lines. Since both activities give rise to agency issues, it is important22

to consider whether delegation of either one or both activities is profitable. This ultimately depends
on how the productivity of managers compares with their ability to divert resources.
Spinoffs. For a given pattern of Ii , the founder delegates spinoff creation to a manager ifΣ ≡ (1 − a) + (e − ωJ )a(1 − SJ ) > 1,

(28)

that is if the founder expects that by investing in spinoffs he can multiply the undistributed cash
flow by a factor Σ > 1 .
i
R&D. If Σ > 1, the founder delegates R&D to a manager if, for a given Π
Zi ratio, the in-houseinnovation return is higher under R&D delegation than under no delegation (see 25):1h(1 − SI )> .
Σ + ωI h(1 − SI )
Σ(29)

If Σ ≤ 1, the firm does not invest in product expansion, and the condition under which R&D
delegation is optimal for the founder is (see 25)h(1 − SI )> 1.
1 + ωI h(1 − SI )(30)

Condition (29) reveals that there is some interaction between the two delegation decisions. If thefounder has opted for investing in spinoffs, the R&D delegation threshold can be satisfied moreeasily. In the quantitative Section 7 we focus on an economy with a set of parameters that satisfies
the two surplus conditions (28) and (29), the two managers’ participation constraints (13) and (17),and that guarantees an interior solution for the governance variables (SI , SJ , a, ωI , ωJ ).

4.3

Households

The representative household has L members. Each household member is endowed with one unitof labor supplied inelastically to the final good sector. Consequently, the labor supply is L. Somehouseholds are also randomly chosen to perform managerial tasks in addition to labor activities in
the final good sector. In view of the welfare analysis, we keep separate the income and consumption
patterns of managers from the rest of the households. To simplify the analysis, managers consume

23

immediately all their contractual and non-contractual earnings. In particular, managers sell newly
created firms to households who seek to invest their savings. Anticipating the general equilibrium
property of symmetry across intermediate firms, the total managers’ consumption is

C m = N {[ωJ e(1 − SJ )a + eSJ a][Π − I − ωI h(1 − SI )I] + ωI h(1 − SI )I + eSI I}.

(31)

Households make their intertemporal consumption-saving choice according to preferences
Z ∞

−ρt

e

U (0) =
0




C (t)
L (t) log
dt,
L (t)

L (t) = eλt ,ρ > λ,(32)

where 0 is the point in time when they make decisions, ρ is the intertemporal discount rate, andC (t) is consumption, net of that of managers. The initial mass of households is L (0) = 1. They
are subject to the budget constraint

Ȧ = rA + wL − C,(33)

where w is the wage and r is the return on assets.13 These are the overall value of the firms,
including those created by managers with diverted resources.
The consumption plan that maximizes (32) subject to (33) consists of the Euler equationr =ρ−λ+

Ċ
,
C

(34)

the budget constraint (33), and the usual boundary conditions.

5

General Equilibrium

In this section we build the general equilibrium of the model. Because in equilibrium the intermediate sector is symmetric, i.e., intermediate firms charge the same price, produce the same quantity,
and grow at the same rate, we drop the subscript i from all expressions.
We start by expressing the production function of the final good in a reduced form. Using the13This budget constraint is equivalent to one that shows dividend income and capital gains (see the Appendix).

24

property that firms receive N P X = θY from the final producer, we have X = θ2 Y /N . Therefore,
under symmetry (3) reduces to2θ

Y = θ 1−θ · N σ · Z · L.

(35)

This expression makes clear that labor productivity, Y /L, is higher in an environment where on
average firms accumulate a larger stock of knowledge, Z, and have a larger number of products,
which in our set up can be driven by spinoffs. Because the rates of return are increasing in theratio between the volume of production, X , and the firm’s stock of knowledge, it is convenient todefine the state variable
x≡

2
X
L
= 1−σ θ 1−θ
Z
N

(36)

which measures the quality-adjusted size of an intermediate firm. In the rest of the paper we referto x simply as “firm size”.
The number of firms (fueled also by consensual and conflictual spinoffs) increases the productivity of the final good sector through the variety effect and at the same time tends to shrink theaverage firm size as more intermediate firms share the same final market. This competition effectreduces the returns to in-house innovation. Specifically, using the cash flow definition, the returns
to in-house knowledge investment and to equity (entry), (25) and (27), reduce to1h(1 − SI )× α[
− 1 x − ϕ],r = rZ ≡Σ + ωI h(1 − SI )
θ

(37)

and
r = rN ≡Σ 11ẋ[( − 1)x − ϕ − (+ ωI )z] + + z ,βx θh(1 − SI )x(38)

respectively, where z ≡ Ż/Z = hI(1−SI )/Z is the investment rate and Σ ≡ (1−a)+(1−ωJ )ea(1−SJ ) is the spinoff surplus factor defined in (28). As equation (37) shows, the return to in-house
investment in product quality, rZ , is greater in an environment where R&D managers are relatively
skilled (high h) and have limited latitude in diverting resources from in-house R&D activities (low
SI and small ωI ). A larger spinoff surplus factor Σ tends to induce firms to reallocate resourcesfrom in-house investment in product quality to the creation of consensual spinoffs. On the other
hand, as equation (38) shows, a larger Σ results in a higher return to equity, rN , which makes entry

25

more attractive. This competition effect reduces the return rZ to in-house investment in productquality in (37) through a reduction in the firm size x, compounding the direct negative effect of Σ.
Gross Domestic Product (GDP). In our set up, the GDP (G) can be obtained by subtracting
the cost of intermediate inputs N X and the fixed cost ϕZN from the final output Y . Using (36),θ
G = [1 −
P



ϕ
1+
]Y ,
x

and, replacing Y with the expression in (35), we have that per capita GDP is


2θ
G
ϕ
2
](θ 1−θ · N σ Z).
= [1 − θ 1 +
L
x

(39)

There are three channels through which spinoff activities affect the level of the GDP. Spinoffs tend
to increase the number of firms, N . This effect is driven directly by the creation of spinoff businesses
and also by the boost that spinoff activities give to firms’ return on equity (entry), as capturedby the spinoff surplus factor Σ. The increase in the number of firms raises productivity throughthe variety channel, represented by the term N σ , thereby promoting an expansion of per capita
income. On the other hand, the rise in the number of firms relative to the size of the population(N/L ratio) implies a reduction in firm size, x. Having more firms per capita can be detrimental tothe economy because they consume operating resources. Finally, the third channel whereby spinoffs
alter the level of the GDP is through the pattern of Z. As noted above, knowledge accumulation
can be adversely affected by spinoffs because they can limit in-house firm growth.
Assets. Since the only assets in the economy are the firms’ value, we have thatA = NV ,(40)

where N measures all firms, including consensual and conflictual spinoffs. As already discussed,
managers do not save; they sell their assets to households, and consume the proceeds immediately.

6

Steady State and Dynamics

We have now the elements to study the steady state and the dynamics of the economy.
26

6.1

The steady state

We characterize the steady state, a situation in which the firm’s size, x, the interest rate, r, andλthe consumption-output ratio, C/Y, are constant. A constant x implies that Ṅ
N = n = 1−σ (see(36)). This suggests that in the long run the rate of entry is eventually tied to population growth.
However, as discussed in the previous section, the level of N depends on the intensity of spinoffs.
We may have two economies that in the long run have the same entry rate and yet at each point
in time have a very different mass of firms, and differ in the value of wealth.
ẎĊ
Exploiting the property that Ċ
C = Y , using the production function (35) we have that C =λσn + z + λ. Combining this result with (34) and using the result n = 1−σ,r=

σλ + z + ρ.
1−σ

(41)

Substituting this expression in the returns to in-house investment (37) and to equity (entry) (38)and setting ẋ = 0, we obtain


α 1
σ
z= [
− 1 x − ϕ] −λ − ρ,
q θ
1−σ

(42)

and
h(1 − SI )z=
1 + ωI h(1 − SI )





σ
( 1−σ
λ + ρ)β
1
−1−
x−ϕ .
θ
Σ

(43)

According to the in-house investment locus (42), for a given firm size, x, the effort of R&D managers
to divert resources for conflictual spinoffs, represented by SI , and the contractual remedies usedby founders to address this issue, captured by ωI , reduce the attractiveness of investments in firm
growth. Note, in fact, that SI and ωI cause an increase in the internal cost of capital, q, as shownin equation (26). The entry locus (43) states that, for a given rate of in-house investment, thesurplus generated by spinoff activities, Σ, reduces the size of firms in the industry, x. Additionally,
this locus indicates that, for a given firm size x, in an environment with high R&D agency costs,
SI and ωI , firms enter with a plan to invest at a relatively low rate z.
The crossing of the loci (42)-(43) yields the steady-state firm size and in-house investment rate.
This intersection captures the general equilibrium feedback between the two arbitrage conditions

27

just described. The steady-state, general equilibrium effects of any change in agency conditions
can be understood through differentiation of the solutions for x and z.
1Σ
Proposition 1 (Steady State). Letting q̂ = h(1−S+ ωI and q = h(1−S+ ωI , the steady stateI)I)

can be characterized as follows:x∗ =σ[q̂(ρ + 1−σλ) − ϕ(1 − αq̂q )]σ(ρ + 1−σλ) β(1−γ)− (1 − αq̂
Σ
q )

α
z =
q
∗



1
θ −1

,



1
σ
∗
−1 x −ϕ −(λ + ρ),
θ
1−σ

λ
,
1−σ



α
1
∗
∗
r =
−1 x −ϕ ,
q
θ
n∗ =

and
c∗ = (1 − θ) − (λ − ρ)βθ2 .

(44)

Proof. See Appendix.

6.2

Dynamic system

The dynamic equilibrium can be represented by a phase diagram in two variables: the firm size, x,
which is the state variable of the system, and the consumption output ratio, c ≡ YC , which is a jump
variable. While the dynamics may exhibit phases in which either n or z or both are zero, here wefocus on a scenario in which both are positive. Proposition 2 summarizes the result. The Appendix
provides details on the derivation and discusses the conditions for having n > 0 and z > 0.
Proposition 2 (Dynamic System). Let x(0) > xZ and x(0) > xN , where the expressions of xZ
and xN are reported in the Appendix. Assume an interior governance equilibrium, that is one inh(1−SI )> 1, and the managers’
which (12), (15), (16), (23), and (24) hold, with Σ > 1, Σ+ωI h(1−SI )participation constraints (13) and (17) are satisfied. The dynamic system of the economy can becharacterized as followsc = (1 − θ) − (λ − ρ)βθ2 ,
28

(45)

ẋ
= λ − (1 − σ)n,
x

(46)

where
n=

Σ 1
ϕ
1
z
[( − 1 − ) − (+ ωI ) ] + (λ − ρ),β θxh(1 − SI )
x

(47)

and


1
h(1 − SI )ασΣ
1z(x) = {(−)[− 1 x − ϕ] − ρ(1 − σ) − σλ},ψ Σ + ωI h(1 − SI )
βx
θ

(48)

1
where ψ ≡ 1 − σΣβx ( h(1−SI ) + ωIi ).

Proof. See the Appendix.

7

Quantitative Analysis

The objective of this section is to quantify the short- and long-run aggregate effects of spinoff
activities. We are interested in the effects on firm entry and on incumbents’ investments, and theimplications for growth and welfare. We are also interested in the feedback from the macroeconomy
to the intensity of spinoffs. In carrying out the quantitative analysis, in line also with the empiricalanalysis of Section 3, we alter the degree of protection of incumbents’ investments, as captured bymanagers’ effort cost in diverting R&D and spinoff resources (δI and δJ , respectively).

7.1

Calibration

The baseline parameterization of the economy is displayed in Table 2. While we especially referto the Italian data in Section 3 to match spinoff activities, we choose other macroeconomic values
in line with those typically adopted by the literature for modern growing economies, especially theUnited States. We set the population growth λ = 1.2%. This is close to the population growthrate of the United States and is also close to the population growth of the Italian provinces during
the 1995-2010 period (the sample period for the data used in Section 3). The social return tovariety is determined through (46) with ẋ = 0: σ = 1 − λ/n. The Italian Chambers of Commerce
report a firm entry rate of about 0.8% for 1995-2006, close to population growth. This would
imply σ = 0. The World Bank Entrepreneurship database, however, indicates that from 1982 tothe 2008 crisis firms’ net entry rate in other advanced countries was higher – for instance it was29

2.5% in the United States, implying σ = 0.6.14 We choose a baseline σ = 0.25, with associatedn = 1.61%. The private return to quality, α, is set to 0.30. In Jones and Williams (1998) the social
rate of return on R&D – a proxy for product quality investment – is in the 30-100 percent range,
with R&D spillovers, 1 − α, of 70%. While the implied spillovers could appear large, others havecalculated similar spillovers through alternative methods for estimating uncompensated externalbenefits (Baumol, 2002, pp.133-134). In the United States, markups have been on the rise for
decades, but they appear to have stabilized in recent years. According to Jaumandreu (2022), who
combined data of the Survey of Manufacturers and Census of Manufacturers, the markup in the
U.S. manufacturing sector from 2010 to 2020 was between 0.24 and 0.30, depending on the year ofreference and the adjustment for the cost of capital. Because in our framework the monopolistic
price is set by intermediate firms, we use this kind of empirical evidence rather than that on final
products. Thus, we pick θ=0.79 which implies a monopolistic price P of 1.27. We set ρ = 3% andtarget a growth rate of per capita income, gG/L , of 2%, and an interest rate of 5%. We choose
the growth rate to reflect the performance of an economy that has a relatively stable growth. The
parameters ϕ and β are set to match gG/L = 2% and a long-run saving rate (s = 1 − C/G) of
about 10.8%. According to data from the Bureau of Economic Analysis, the U.S. gross nationalsaving rate in the post-war period fluctuated between 15% and 20%. Allowing for a depreciation
rate of 8%, this would imply a net saving rate, as a ratio of GDP, in the interval of 7-12%. Our
calibration matches a saving rate in the middle of this interval. The resulting investment ratez = y − σn = 1.60%.
Agency costs: spinoffs. We use simple logarithmic functions to represent the utility costs ofdiversion for conflictual spinoffs and of creation of consensual spinoffs: cJ (SJ ) = − log(1 − SJ /bJ )and cJ (ŜJ ) = − log(1 − ŜJ /b̂J ). The spinoff manager’s reaction functions (15) and (16), together
with the founder’s optimal contract for the spinoff manager (23), yield the following three equations:δJ,e(1 − ωJ )

(49)

δ̂J
,
ωJ e(1 − SJ ) + eSJ(50)

SJ = bJ −a = b̂J −14

See also Lee and Mukoyama (2015) for the entry rates in the U.S. manufacturing sector.

30

and
(e − ωJ )aδJδ̂J e(1 − SJ )1
+ (e − ωJ )(1 − SJ )= (1 − SJ )a.
2e (1 − ωJ )(ωJ e(1 − SJ ) + eSJ )2(51)

Further, for the delegation of spinoff activities to be profitable for founders it must be that Σ > 1.
Agency costs: R&D. We also rely on a logarithmic function for the cost of diverting R&D
resources: cI (SI ) = − log(1 − SI /bI ). The parameters (δI , bI ) scale up the cost of diversion effort
and regulate R&D managers’ diversion ability. We choose these two parameters by solving for them
the two equations:
SI = bI −δI,e(1 − hωI )and(1 − SI )h = ΣδI h,(e − hωI )2

that follow from the R&D manager’s reaction function (12) and contract (24), taking ωI , SI , andh as given.
In total, we have eight governance parameters to fix: δI , bI , δJ , bJ , δ̂J , b̂J , h, e. By targeting thevalues (SI , SJ , a, ωI ,ωJ ) the above five equations give us five constraints. Two additional constraints
come from the spinoff surplus factor Σ and the internal cost of capital q. Thus, we are left witha free parameter that we use to insure an interior governance equilibrium. The parameters δI andδJ scale up managers’ diversion effort cost, whereas bI and bJ regulate the curvature of the costfunctions. A stronger investment protection, as determined by higher quality of the judiciary andmore stringent laws, is captured by an increase in the diversion cost parameters δI and δJ . The
parameter δ̂J indicates how hostile is the business environment towards the creation of spinoffs on
the part of incumbent firms, whereas b̂J captures managers’ spinoff efficiency.
We proceed to the targeting of the seven governance variables (SI , SJ , a, ωI , ωJ , Σ, q) using
a mix of corporate finance, industry, and macroeconomic evidence. Lacking compelling evidence
that allows us to distinguish between the intensities of the two types of diversion (in R&D andspinoff activities), we simply set SJ = SI and ωI = ωJ . This simplifying assumption, which has
the advantage of bringing clarity to the calibration exercise, turns out to generate results that are
in line with available evidence.
The Capitalia Survey on Italian Manufacturing Firms indicates that the R&D over sales ratio31

is about 4.1% and that around 40% of R&D expenses is absorbed by radical product innovations
which lead to new products. Only a fraction of the radical product innovations, however, is later
developed through spinoffs. In particular, the survey suggests that about a third of them are carriedout through spinoffs while the rest remain within incumbent firms. This evidence thus implies thataF
F
pX = 0.00547. Anticipating a steady state value of pX around 0.1, we therefore target a = 0.05.

Next, we use evidence about the relative share of conflictual and non-conflictual spinoffs, RC .
Our computations with the Italian data in Section 3 indicate that such ratio is between 20 and 25
percent. Using equations (8)-(10), we then have

RC =

SI
I
ṄJi + ṄIi
SJ+
= 0.223.
=
1 − SJ
1 − SJ aF
ṄFi

(52)

In steady state I/F is about 0.072. With a = 0.05, and recalling the condition SJ =SI , the above
equation yields SJ = 0.08. For the managers’ compensation we combine information regarding
managers’ total pay relative to the asset evaluation of firms (Glovert and Levine, 2017), together
with data on the share of managers’ pay accounted for by variable compensation, including bonuses(Chenevert and Hoffman, 2017). These studies imply that managerial compensation is around 0.2%of the assets. In our model the managers’ compensation over asset ratio is Rm =(ωI Ż + ωJ ṄF )/V .
Using the free entry condition V = βX, and the condition ωI = ωJ , after some algebra we obtain
Rm =

F
ωI z
[ + ea (1 − SJ )].
β x
XFEvaluating z, x, and X
on their steady state value using the targeted governance variables, we

obtain Rm = 0.2% with an ωI = 0.25.
We set the two remaining governance parameters e and h so as to imply values for Σ and q
marginally above and below 1, respectively. By doing so, we have e = 1.4 and h = 1.48. We
also verified that under our governance targets the model generates a spinoff rate similar to theempirical evidence. Section 3 reported a spinoff rate (spinoffs over total entries) for Italian firms
of approximately 25%. Klepper and Sleeper (2005) calculated spinoff rates of 17%, 20%, and 24%
for the U.S. laser, automaker, and disk drive industries, respectively. Using equations (8-10), the32model’s spinoff rate, RS =N (ṄFi +ṄJi +ṄIi ), can be written as
Ṅ

RS =

e
F
z
(a + SI).
nβ X
xh(1 − SI )(53)

Employing the steady state values for F/X, z, n, and x, and the governance parameters detailed
above, we obtain RS = 25.5%. Table 2, Panel D, summarizes the economy steady state values.

7.2

Spinoff activities in the long run

This section compares the long-run equilibrium (steady state) of economies that differ only withrespect to investment protection. In our model a stronger investment protection can be represented
by a higher δI or a higher δJ , which measure managers’ cost of diversion in R&D and spinoff
activities, respectively. An increase of δI and of δJ captures reforms of the legal system thatmake it more difficult for employees to divert resources from funds allocated to the improvementof existing products or to the creation of new ones. Such reforms could, for instance, allow greater
latitude to firms to offer labour contracts and covenants with restrictive non-competition clauses,
or more in general that restrict the employees’ freedom in enterprising activities that could hurt thecommercial interests of the current employer. They could also introduce more stringent norms on
the relationship between newly created spinoffs and parent companies or on the competition among
divisions of multi-product firms.15 Although in practice and in the law the protection of investments
in product creation and in quality enhancement tend to be interwined, in the quantitative analysis
we mantain such distinction to disentangle the key mechanisms.
Specifically, we consider three scenarios. In one only the parameter δI varies across economies.
Hence, we compare economies that differ in legislation or enforcement with respect to the sanctions
applied to agents who use incumbents’ R&D resources to set up new firms. In a second scenario
we vary the parameter δJ , that is, we compare economies that differ in the severity of sanctions
for agents that divert resources during spinoff activities. In a third scenario the two parameters δIand δJ move in lock-step so that their ratio is held constant. This is intended to capture variationin legislation that is neutral with respect to the two types of diversion. Finally, we interpret the15
Section 2125 of the Italian Civil Code regulates employees’ non-competition clauses; article 2557 of the Coderegulates firm competition.

33

evidence of Section 3 with the lens of the calibration exercise and conclude that it matches wellwith the scenario of neutral investment protection variation (constant δJ /δI ) or with one in whichinvestment protection has a relatively more pronounced effect on the diversion cost δI than on δJ .
The main takeaway message of the quantitative experiments is that a priori investment protection can have ambiguous effects on the incidence of spinoffs in firm entry and on incumbents’ R&D
investment in product quality, depending on whether the variation of investment protection affects
relatively more δI or δJ . Specifically, the experiments show that firm growth, z, is increasing inδI while the spinoff rate, RS , declines in δI : that is, economies that especially protect the resultsof R&D and, for instance, impose strong sanctions in labor contracts between owners and R&D
managers are predicted to reduce the incidence of spinoffs and boost incumbents’ R&D investments
and growth. Conversely, firm growth, z, is decreasing in δJ and the spinoff rate, RS , increases inδJ : economies that focus on disciplining corporate spinoff activities or the relationship between
newly created spinoffs and parent companies can raise the incidence of spinoffs, while reducing
incumbents’ R&D investment in product quality. The quantitative results further suggest that a
broad legal reform enhancing overall investment protection (thus raising both δI and δJ ) shouldhave effects more in line with those of a reform that increases δI , reducing the incidence of spinoffs,
boosting incumbents’ R&D investments, and lowering the relative importance of conflictual spinoffs
among spinoff entrants.
It is useful to recall here that in our framework the expression for the internal cost of capitalΣ+ ωI contains all the governance wedges. Any reduction of the internal cost of capital,q = h(1−SI)

for instance due to a reduction in the R&D agency costs, incentivizes in-house innovation, z, for agiven firm size. In addition, in general equilibrium incumbents’ investment in in-house innovationis also driven by changes in firms’ size. Following firm growth z in steady state is of great interest
because any governance variation that affects the steady state per capita income growth rate, σn+z,
goes through z; the rate of entry n is pinned down by population growth, which is exogenous.

Scenario 1: Investment protection and R&D activities (δI ).

Section 4.2.3 showed thatwhen the diversion of R&D resources is very costly, i.e. with high δI , an R&D manager exerts alower diversion effort SI , for a given ωI , and the founder finds it optimal to raise the compensation34

ωI . This further reduces diversion, SI . Hence, for a given R&D investment I, the number ofconflictual spinoffs created by R&D managers declines with δI , which tends to drive down the
spinoff rate. The decline of R&D diversion SI also leads to a reduction of q, and therefore createsstronger incentives for incumbent firms’ R&D investment and growth z.
In general equilibrium, there are additional mechanisms to consider. Figure (1) helps understand these mechanisms. It confirms that at a higher δI , R&D managers receive a more generous
compensation and divert less R&D resources. The reduction of the cost of capital, q, leads to higher
firm growth, z. This means that the base from which R&D managers can divert resources expands.
Hence, in general equilibrium there are contrasting effects associated with a stronger investment
protection that goes through an increase of δI . On the one hand, this diminishes the fraction of
R&D investments diverted to conflictual spinoffs. On the other hand, it expands the diversion base,
I. In our calibrated economies the former effect dominates the latter. This explains the decline of
the spinoff rate, RS , together with the decline in the ratio of conflictual over consensual spinoffs,
RC . Observe that all the results are explained through the agency relationships between founders
and R&D managers. Spinoff managers’ diversion does not react to changes in δI , and the decision
about the rate of investment in spinoffs, a, is neutral with respect to δI .
Scenario 2: Investment protection and spinoff activities (δJ ).

Figure (2) compares thegovernance values SJ , ωJ , and a for economies that differ only in the parameter δJ , which captures
the strength of investment protection in preventing the diversion of spinoff resources. Similar to
Figure (1), Figure (2) also shows the aggregate consequences of variations of δJ .
While an improvement in investment protection that takes the form of rises in the value of δI is
associated with stronger incumbents’ growth and fewer spinoffs, one that increases δJ may havethe opposite effects on these two quantities. Figure (2) shows that the spinoff rate, RS , rises inδJ despite the negative effect that this has on SJ , i.e., on the creation of conflictual spinoffs byspinoff managers. Indeed, the improvement in the agency relationship between founders and spinoff
managers induces founders to reduce ωI and increase ωJ (a founder knows that spinoff resources
will be diverted to a lower extent, and hence tends to compensate the spinoff manager more). The
increase in ωJ stimulates the creation of consensual spinoffs. Moreover, the reduction of R&D

35

managers’ compensation ωI induced by a higher δJ triggers an increase in conflictual spinoffs by
R&D managers. Together these effects outweigh the reduction in conflictual spinoffs of spinoff
managers, overall raising the spinoff rate RS . On the other hand, improving the protection of
spinoff activities reduces incentives for firm growth, z, for two reasons. First, it tilts the arbitrage
between product quality improvement and product development towards the latter, as shown by Σentering at the denominator of the in-house investment condition (38) and at the numerator of theequity return condition (39). Indeed, Figure (2) shows that at a higher δJ firms allocate a larger
share of cash to product expansion through spinoffs, a, and confirms that founders’ surplus fromdelegating the creation of startups, Σ, improves. Second, more favorable entry conditions (i.e., ahigher return to equity) in economies with higher δj lead to a smaller average firm size, x, further
reducing the incentives to invest in product quality.

Scenario 3: A broad legal reform (constant ratio δJ /δI ).

The analysis so far indicated thatin the parameter region of our calibrated economy, δI and δJ can generate contrasting effects on
incumbents’ long-run growth and on the incidence of spinoffs. A comparison of Figures (1) and (2)suggests that firm growth is relatively more sensitive to variations in δI than to variations in δJ .
To gain additional insights, we compare economies that differ in investment protection but havethe same ratio δI /δJ . Put differently, we compare economies with different strengths of investment
protection, but this is not biased towards preventing one or the other type of diversion. As noted,
this type of exercise is likely to mimic the effect of a broad legal reform or of an overall improvementin the efficiency of courts, such as that considered in our empirical analysis of Section 3.
Figure (3) shows the results, plotting the aggregate and governance variables against δI . The
effects on z and on the spinoff rate, RS , echo those shown in Figure (1) for scenario 1, where δJ was
kept constant. The changes in R&D agency relationships also mimic those of Figure (1). Differently
than in scenario 1, there is a milder decline of the spinoff rate, RS , as the reduction of SI is partly
counterbalanced by the effects of a reduction of SJ . Under such unbiased improvement in thequality of investment protection, firm growth and long-run per capita income growth increase. The
predictions of the model under this exercise are consistent with the evidence of Section 3. Recall, in
fact, that in the empirical tests we obtained that a higher court efficiency is associated with a lower

36

spinoff rate (columns 1-4 of Table 1) and with a lower ratio of conflictual over consensual spinoffs(columns 5-6 of Table 1). We also found that it is associated with larger innovation of incumbents
aimed at product improvement (Panel B of Table 1), again in line with the model predictions.

7.3

Shocks and spinoff activities in the short and medium runs

In this section, we use the dynamics of the model to investigate the short- and medium-run effects
of a reform that makes it harder for managers to divert resources from incumbent firms. While abroad reform is likely to increase both types of diversion costs δI and δJ , because these affect theadjustment process differently, we first consider a variation of one type of diversion cost at a time.
In particular, we first consider an unanticipated shock that causes an immediate and permanent
increase of δI , keeping δJ fixed, and then one which causes a permanent increase of δJ , but that
does not affect δI . Figures (4) and (5) plot the adjustment of the policy functions z(x) and n(x)and of the two spinoff ratios RS (x) and RC (x) with respect to the firm size, the system’s statevariable. Figure (6) shows the economy’s response through the time series of these four variables.
Before a shock hits, the economy is on a steady state. We denote values for the pre-shock steadystate with subscripts 0 and values for the new steady state with an asterisk. As the figures show,
a variable, such as z or n, typically exhibits an initial jump in the immediate aftermath of theshock, followed by a slow movement toward the new steady state. The initial jump captures theshort-run response, for a given level of industry competition. The subsequent movement is driven
by industry dynamics, represented through a rise or fall of the average firm size, x.
A key insight of the dynamic responses is that the dynamic general equilibrium effects associatedwith the adjustment of the market structure reinforce the effects discussed in Section 7.2. Following
a shock to δI (i.e., a reform that better protects R&D results), the reduction in spinoff creationand the overall decrease in firm entry tend to make incumbent firms larger, further boosting inhouse R&D investments. By contrast, following a shock to δJ (i.e., a reform that better protects
incumbent firms’ spinoff activities) the increase in spinoff creation and the overall increase in firm
entry tend to make incumbent firms smaller, further depressing in-house R&D investments.

37

Shock to δI .

Suppose that a legislative reform causes the R&D diversion parameter δI to go up

so as to reduce the level of diversion SI by half (∆δI = 0.0044). Figure (4.A) shows that in theaftermath of the shock the in-house investment rate and the entry rate move in opposite directions.
The decline in the internal cost of capital, q, induces firms to boost up their in-house investmentin product quality z. Because households allocate a relatively larger share of income to in-houseinnovation, they reduce the funding for the creation of de-novo firms. The spinoff rate, RS , drops.
This indicates that the direct effect of the rising diversion cost, δI , is sufficiently strong to outweighany advantage R&D managers may derive from an enlarged diversion base, I. As the economy
moves towards the new steady state, i.e. as x rises from x0 to x∗ , firms further step up their inhouse investment z. De novo entry also rises progressively as firms’ expected profitability improves.
This implies that the saving rate goes up during the transition (Figure (6)). The transition has
relatively small effects on the spinoff rate, RS , and on the share of conflictual spinoffs, that is,a substantial portion of the effects on spinoffs show up immediately after the shock. Table 3.A
summarizes the short- and long-run changes of the macroeconomic variables.
Shock to δJ .

A reform that tightens up managers’ control on the use of firms’ spinoff investments,

aF , triggers effects that may be strikingly different than those illustrated above. Figure (4.B) showsthe effects of a permanent increase of δJ that causes SJ to drop by half (δJ = 0.0339). As a resultof the increase of δJ , firm size declines from x0 to x∗ , the rate of investment, z, drops from z0 andthen descends toward z ∗ , and n first jumps up from n0 and then moves smoothly to a lower level n∗ .
The immediate drop in z reflects the fact that the shock induces firms to rebalance their investment
outlays towards consensual spinoffs and away from product quality improvement. The changing
investment incentives can be understood from the in-house arbitrage condition (37), which contains
the spinoff surplus factor Σ in the denominator. The subsequent decline in z, however, is due to anindustry adjustment effect. In particular, the greater flow of consensual spinoffs reinforces entry
and, hence, reduces the average firm size,16 which explains the decline in x from x0 to x∗ . The
entry of ex-novo firms also accelerates as households expect higher capital gains from new firms, asthese can appropriate a larger share of the value of their spinoffs.
The time series of the saving rate and of the per capita GDP growth rate, shown in Figure (6),16

The share of conflictual spinoffs RC declines.

38

imply that in the short run the decline in firm growth is compensated by firm entry. Nevertheless,along the transition both the saving rate and the rate of per capita GDP growth slightly decline.
This is also due to the stronger competition effect that reduces the average firm size, and thus thereturn on in-house investment, as noted. The short- and long-run variations of key macroeconomic
variables are summarized in Table 3.B.

Taking stock.

The two experiments demonstrated that a policy reform that affects δI generates

short- and medium-run macroeconomic effects that go in the opposite direction of those that stemfrom a rise of δJ . Further, the magnitude of the quantitative responses of firm growth, entry, andper capita GDP are quite different in the two cases (recall that for comparative purposes we built
the shocks so that each induces the relevant margin of diversion SI or SJ to go down by half).
For instance, the average rate of in-house investment goes up by 0.0256 percentage points in thelong run in response to a shock to δI but declines only by a fifth of that when the shock alters δJ ;and the drop in the entry rate, n, in the immediate aftermath of the shock to δI is about twice aslarge as the rise of n after the shock to δJ (see Panels A and B of Table 3). When the shock altersboth δI and δJ by the same size described above for the stand-alone shocks, the macroeconomic
responses are in between of those for the stand-alone experiments, but closer to the δI -experiment.
For instance, in the stand-alone δI and δJ experiments the short-run variations of z are 0.0173
and -0.0011 percentage points, respectively, whereas in the joint experiment the variation of z is
0.0163 percentage points; the long-run variations of z are 0.0256 and -0.0047 percentage points inthe stand-alone experiments, and 0.021 percentage points in the joint experiment.

7.4

WelfareWe now study the welfare consequences of investment protection shocks.17 To keep the analysis
simple, we focus on the welfare of households excluding the consumption of managers, that is of
the households that solve the intertemporal consumption-saving problem and whose preferences
17

In the Appendix we discuss how the equilibrium of our economy compares with the first best allocation.

39

are described in (32). Using the reduced-form production function (35), this can be written asu = logCL= θ̂ + log c + σ log N + log Z,

(54)

2θ
where θ̂ ≡ 1−θ
log θ. Breaking down per capita consumption into the three variables c, N , and Z,

presents the advantage of weighing the relative importance of the forces that affect households’
welfare. In our economy, the equilibrium consumption-output ratio c = (1 − θ) − (λ − ρ)βθ2
is constant and is not affected by investment protection shocks. Such shocks affect productivity
directly through the product variety, N . For example, an expansion of product lines, which are
fueled by consensual and non-consensual spinoffs and by ex-novo entries, makes the final good sector
more productive. This productivity improvement is governed by the social externality parameter, σ.
Therefore, a reform that restricts spinoffs tends to slow down variety-driven productivity gains. As
noted, however, such a reform can stimulate the growth of incumbents, as it curbs non-consensual
spinoffs and raises the return on in-house investment in product quality. The faster improvementof the quality of intermediate products, Q, means a faster labor productivity growth in the final
good sector, yielding wage gains. The contrasting productivity effects of a policy reform translatea priori into an ambiguous impact on welfare.
Following the layout of Section 7.3, we examine the welfare implications of a change in δIand δJ , separately, and then of a contemporaneous change in both parameters. The consumption
equivalent welfare effects of the shocks are illustrated in Figure (7). The fraction of consumption, χ,that needs to be given to (or taken from) the representative household of the untreated economy, inorder to equate its utility flow with that of the household of the treated economy, can be obtainedC̃by solving u( CL )(1 + χ) = u( L ), where (∼) refers to the treated economy. Using (54), this implies

log(1 + χ) = log

c̃
Ñ
Z̃
+ σ log+ log .
c
N
Z

(55)

Figure (7) decomposes the welfare change into its the three main components: the number of firms,
the stock of knowledge, and the consumption output ratio. To account for the role of spinoffs inSÑ
entry, the figure also plots log N
S . Both economies share the same initial condition, corresponding

to the steady state of the untreated economy.

40

An improvement of the R&D governance (higher δI ) has an overall positive welfare effect, χ,
at every time horizon. This is mostly driven by the significant productivity gains that stem fromfirm growth, Z. However, the slowdown in business dynamics, N , caused by the more modest firm
entry, reduces the welfare gains fueled by firm growth. The entry effect, however, is one order ofmagnitude smaller relative to the firm growth channel. For instance, five years after the shock,there is a consumption equivalent welfare gain, χ, of 0.104%. After five years, the treated economy’s
firm knowledge, Z, is 0.1082% higher while the number of firms, and hence the number of products,
is 0.0158% lower. Using (55) with a product variety social parameter σ = 0.25, we calculate thewelfare gains to be 0.1082%+0.25(-0.0158%)=0.104% after five years. Additionally, the spinoff
rate is 3.77% lower in the treated economy than in the untreated economy, which corresponds toa decline of the initial spinoff rate, RS , by about 1 percentage point, from 25.5% to 24.5%. In
other words, while the overall response of entry is relatively small compared to firm growth, there
is a considerable entry composition effect: after 5 years, the treated economy has one fewer spinoff
out of 100 firms than the untreated economy. Therefore, while spinoffs reduce the consumption
equivalent welfare by σ × 1% = 0.25% (which is about two and a half times the welfare change
driven by firm growth Z), ex-novo firms almost entirely compensate for this welfare decline. This isbecause a higher δI makes firms more profitable and therefore attracts more ex-novo firms. Figure(7.A) shows that the log difference of the spinoff rate declines slightly over time, suggesting that
there is some additional long-run welfare effect attributable to the reshuffling between ex-novo andspinoffs firms. Although the negative welfare effect increases in magnitude over time, it remains
small relative to the welfare gains from firm growth, Z, which also continue to increase.
The welfare consequences of an improvement of the spinoff governance (higher δJ ), depicted in
Figure (7.B), are opposite in direction to those resulting form the shock to δI . The spinoff rate
differential increases by about 1%, as opposed to the -3.77% observed in the previous experiment,
and the log differential in the overall number of firms is of the same order of magnitude (but withthe opposite sign) of that observed in the δI −experiment. The slowdown in firm growth is about
one fifth of the acceleration recorded in the δI −experiment . Overall, in terms of consumption
equivalence, the welfare decline in the δJ −experiment is one order of magnitude smaller than thewelfare expansion in the δI −experiment. Figure (7.C) shows the welfare gains when both δI and41δJ are increased simultaneously, resulting in halving the rates of diversion SI and SJ . The figuresuggests that the welfare implications depend relatively more on the agency relationship between
incumbents and R&D managers than on that between incumbents and spinoff managers.

Sensitivity of welfare effects.

How do the welfare results depend on the efficiency with which

managers use resources to generate spinoffs? We replicated the δI and the δJ experiments foran economy that has a value of e 20% larger than the baseline economy. In an economy whereconsensual spinoff creation is more profitable, Σ is larger. Nevertheless, conflictual spinoffs become
more profitable too. This induces managers to divert relatively more resources and owners topropose more generous contracts to managers. For instance, in the baseline economy SI = SJ =0.08, whereas in the high-e economy SI = 0.175 and SJ = 0.0975. In our experiments, however, theincrease in agency costs is more than compensated by the larger capital gains due to the consensual
spinoffs. Specifically, the producer surplus Σ is about 1.30% larger in the high−e economy. Panel
A of Figure (8) shows that an improvement in the R&D governance has a more modest but still
positive welfare effect in the high-e economy. Indeed, in this economy the in-house investment
arbitrage condition is less sensitive to δI , which explains why firm growth, Z, reacts relatively lessto the shock. Conversely, the welfare change of the high−e economy is more pronounced in thecase of an improvement of the spinoff governance (higher δJ ). Relative to the baseline economy,
the shock induces a more robust ex-novo entry, which adversely affects incumbents’ growth, Z.

7.5

Robustness and further sensitivity analysis

The data in Section 3 suggest that there is some variation in the incidence of consensual spinoffs(e.g., due to cross-industry technological differences). In our set up, this can be captured by theparameter δ̂J which governs the cost of creating consensual spinoffs. Motivated by the evidence, we
study the sensitivity of the responses to a reform with respect to the consensual spinoff costs of theindustry. Appendix Table A2, Panels A-C, and Appendix Figure (A2) summarize the outcomes of
the above experiments, but for an economy where δ̂J is 50% higher. The outcomes are quite close
to those obtained for the baseline economy. For example, in the δI -experiment, R&D diversion
is about one-tenth higher in the high-δ̂J scenario than in the baseline economy. The immediate42responses of entry and of in-house investment also show little difference relative to the baseline
economy. The changes of z and n are 0.0171 and -0.0028 percentage points in the high-δ̂J scenario,
whereas the corresponding values for the baseline economy are 0.0173 and -0.0029 (see Panel A of
Table 3 and of Appendix Table A2). Robustness to altering δ̂J also emerges when considering theshock to δJ and the joint shock (refer to Panels B and C of Table 3 and of Appendix Table A2).
A further useful sensitivity analysis regards the influence of knowledge externalities. In ourframework, an intermediate firm benefits from the average knowledge developed by other firms,
according to the parameter 1 − α. Indeed, a firm’s in-house return elasticity to its own knowledge
is α. As noted, a reader could wonder whether the knowledge externalities implied by our choice of α
are somewhat large (in the baseline economy α = 0.3). We then replicated the δI and δJ experiments
in an environment with weaker knowledge externalities, or, equivalently, with a stronger private
knowledge appropriation (α = 0.35). The findings are in Panels A-B of Appendix Table A3. The
effects of the δJ experiment (a reform that raises the protection of spinoff investments) are very
close to the baseline, while, if anything, those of the δI experiment (an increase in the protectionof R&D investments) are somewhat stronger when α is higher. Our calculations reveal that the
short- and long-term changes of the investment rate, z, are approximately one-third stronger in theeconomy with α = 0.35. In neither experiment the overall entry rate is sensitive to α. Nevertheless,the decline of the spinoff rate in the δI experiment is more significant in the high-α economy, where
the drop (1.8 percentage points) is about twice as large as that of the baseline economy.18 Further,
the welfare response in the δI experiment is about one-third stronger in the economy with α = 0.35.

8Investment Protection and Subsidies

To gain additional insights, we study alternative policies aimed at promoting business dynamics
and R&D efforts. In one set of experiments, we contrast a legal reform that attempts to incite entry
by weakening investor protection with a more traditional policy that subsidizes the creation of new
firms. In a second set of experiments, the government tries to stimulate growth by promoting
in-house investments in two alternative ways: by strengthening R&D investment protection or byproviding an R&D subsidy. In any experiment involving a subsidy, this is fully financed through a
18

The drop of the ratio of conflictual over consensual spinoffs is about two times larger in the high-α economy.

43flat tax on labor income – that is, the government keeps a balanced budget in every period. In what
follows, we first explain how the baseline model can be amended to allow for the two subsidies.19
Then, we discuss the two sets of experiments in which only one subsidy at a time is considered.
Since the labor supply is inelastic, the tax is non-distortionary. With a tax rate τ , the perperiod tax revenue is τ wL. If the government provides an entry subsidy proportional to the entry
cost, a new firm entry cost is β(1 − γ)X. Similarly, an R&D subsidy allows an incumbent firm tohave a lower cost of knowledge creation. Therefore, (7) becomes (dropping the index i)Ż = h(1 − SI ) · I(1 + ξ).

(56)

The parameters γ and ξ govern the size of the entry and R&D subsidies, respectively.
The introduction of the labor tax transforms the household budget constraint (33) as follows

Ȧ = rA + (1 − τ )wL − C.

(57)

As noted, the tax revenue τ wL covers the entry subsidy γβX Ṅ and the R&D subsidy ξIN :τ wL = γβX Ṅ + ξIN.

Using (5), the government budget constraint can be written as

τ = γnβθ2 + ξIN.
YThis can also be rearranged as

τ = γnβθ2 + ξzθ2x h(1 − SI )(1 + ξ)

(58)

where we made again use of (5) and of (56). The dynamics of this extended version of the modelwith subsidies are more articulated than those of the baseline model, because the consumption
output ratio is no longer constant over the transition (observe that, for a constant τ , the subsidy
19

See also the Appendix for details on this extension with subsidies.

44

rate depends on the firm size, x). Here, we focus on salient steady state properties.

λSteady State. The steady state entry rate is still n∗ = 1−σ. The firm size is similar to that in

Proposition (1) except that β is replaced by β(1−γ) and that q̂ and q now represent the expressionsq̂ = h(1−SI1)(1+ξ) + ωI and q = h(1−SΣ+ ωI , respectively:
I )(1+ξ)x∗ =σ[ϕ(1 − αq̂q ) − q̂(ρ + 1−σ λ)].
β(1−γ)1σ(1 − αq̂
)
−
1
−
(ρ
+
λ)
q
θ
1−σ
Σ

(59)

Observe that the firm size declines with the intensity of the entry subsidy γ, while it increases with
the intensity of the R&D subsidy ξ. Consequently, in an economy with a larger entry subsidy γ
the investment rate is smaller as this depends on firms’ scale: in an economy with smaller firms,
the rate of investment is lower (see the expression for z ∗ in Proposition 1). This result is reversed
when there is a larger R&D subsidy ξ. The steady state expressions for the firm size, innovation
rate, and entry rate, x∗ , z ∗ and n∗ , respectively, displayed in Proposition (1) will also hold provided
that β is replaced with β(1 − γ ∗ ). The spinoff rate becomes(RS )∗ =

F ∗
z∗
e
[a(
)
+
S
]
I
n∗ β(1 − γ) X
xh(1 − SI )(1 + ξ)F ∗or being more explicit on the ratio ( X)(RS )∗ =

e
1
ϕ
1
z∗
z∗
{a[
−
1
−
−
(
+
ω
)
]
+
S
}. (60)II ∗n∗ β(1 − γ)θx∗h(1 − SI )(1 + ξ)x∗x h(1 − SI )(1 + ξ)This expression says that the entry subsidy γ has a positive effect on the spinoff rate, both directly
and through x∗ . The positive feedback through x∗ , however, is diluted by the decline of theinvestment rate z ∗ , that is, by a decline of the resources that can be employed in conflictual
spinoffs. The effect of the R&D subsidy ξ on the spinoff rate is instead ambiguous. Nevertheless,if R&D investments are sufficiently responsive to the subsidy, the spinoff rate goes up with ξ .

Comparing policies: entry subsidies and investment protection.

Consider two policy

reforms. With one, the government loosens up the legislation that inhibits R&D managers to make45

off with incumbents’ R&D investments. This is captured by an unexpected and permanent declineof δI in the baseline economy. In this experiment γ = ξ = τ = 0. With the other policy, thegovernment subsidizes entry and finances it through a labor tax in the manner described above(there are no R&D subsidies, i.e. ξ = 0). To easy the comparison, both experiments cause long-run
firm growth to slow down by 0.1%. In both experiments, the intensification of entry increases thedegree of competition, i.e., the firm size, x, declines, weakening firms’ incentive to invest in productimprovement. While the direction of the change is similar in the two experiments, the magnitudeof the responses is quite different (see Appendix Figure (A3)). In the short run, the introduction ofan entry subsidy causes a more modest slowdown of knowledge accumulation and a milder changeof the spinoff ratios than a reduction of δI . Table A4 shows that z, for instance, drops by 0.0045
percentage points in the case of the entry subsidy and by 0.0671 percentage points in the case of areduction of δI . Conversely, the subsidy shock has a short-term impact on firm entry about twiceas large of that triggered by the legal reform (0.034 vs 0.011 percentage points; see Table A4).
Turning to welfare, following the introduction of an entry subsidy in the short term there is
a visible drop of the welfare χ mostly driven by the introduction of a labor tax, τ . The welfarechange then remains negative at all time horizons because the more intense competition deflates
incumbents’ incentives to invest. Welfare also reacts negatively at all time horizons to a reductionof δI – as noted, the deterioration of the R&D agency issues also makes incumbents more reluctant
to invest in product improvement. The welfare losses, however, are more pronounced in the caseof an entry subsidy, mostly because of the negative wealth effect of the labor tax (Figure 7.D).

Comparing policies: R&D subsidies and investment protection.

As an R&D subsidy

reduces the internal cost of capital q, incumbents are incentivized to increase their in-house R&D
investment. It is then natural to compare this policy with a legal reform that increases the costof R&D diversion (higher δI ). To facilitate this comparison, we introduce an R&D subsidy χthat raises the economy’s long-term growth by the same amount as the δI experiment above, asreported in Table 3. The responses of overall business dynamics and firm growth are nearly identicalin the two experiments (compare Table A5 with Table 3). However, the response of spinoffs differs
considerably: the R&D subsidy increases the spinoff rate modestly (by around 0.1%), whereas this46

decreases by about 7% as a result of the legal reform. Since the subsidy encourages knowledge
accumulation, it also expands the diversion basis. Conversely, the legal reform makes it more
expensive for managers to funnel resources, and therefore to generate spinoff firms.
The welfare consequences of the introduction of an R&D subsidy are almost indistinguishable
from those of the legal reform that raises δI . In fact, the response of n and z is nearly identical in
the two experiments (compare Table A5 with Table 3). While the R&D subsidy results in a dropof the consumption output ratio, such a drop is very small (-0.0003%, as shown in Table A5) anddoes not make a significant difference between the two policies.

9

Conclusion

This paper has investigated the short- and long-run aggregate consequences of spinoffs. The analysis
was motivated by two contrasting views. On the one hand, the law view voices the concern thatincumbent firms may react to the potential diversion of trade secrets associated with conflictual
spinoffs by reducing their investment in knowledge creation and product improvement. On theother hand, spinoffs can be a useful vehicle to spur the rapid dissemination of new technologies:
new businesses bring new ideas. Our empirical evidence supported the notion that the intensity ofspinoffs is influence by the quality of the judiciary and of investment protection.
In the model we assumed that employees with special skills in using investment resources canchannel a fraction of these resources to the creation of spinoffs in conflict with incumbent firms. We
found that a priori investment protection has ambiguous effects on the intensity of spinoff activities
and on the pace of incumbents’ investments in product quality. When the legal system is strong
in protecting incumbents’ expansion of product lines, the economy will experience more intense
spinoff creation, a wider set of intermediate products, but also smaller investments in productquality improvement. However, if the legal system also highlights the protection of in-house R&D
results, the economy will have lower spinoff creation and more intense R&D investments in productquality. Our quantitative analysis revealed that, following a broad legal reform that strengthens
investment protection, the positive impact of the increased amelioration of product quality on GDP
growth and welfare will outweigh the negative effect through the reduced spinoff creation and theresulting slower introduction of new inputs. Thus, the analysis supports the rule of law view.
47

The paper leaves relevant questions open for future research. An aspect worth investigating is
how the relationship between the average rate of innovation and investment protection is affectedby the degree of competition between incumbents and spinoff firms. In a situation in which spinoffs
represent a strong competitive threat, on the one hand, such a threat can further reduce the
incentive to invest – thus the Schumpeterian competitive effect would reinforce the “law view”– but on the other hand it could give a further kick to incumbents’ innovation effort to fend off
spinoffs’ attempt to take over a market share (see, for instance, Aghion et al., 2001; Aghion et al.,2005; and, more recently, Cavenaile et al., 2020). We leave this and other issues to future research.

References
Aghion, P., U. Akcigit, and P. Howitt (2014). What do we learn from Schumpeterian growththeory?. Handbook of economic growth. Vol. 2. Elsevier, 515-563.
Aghion, P., N. Bloom, R. Blundell, R. Griffith, and P. Howitt (2005). Competition and innovation:
an inverted-U relationship. The Quarterly Journal of Economics, 120(2), 701-728.
Aghion, P., C. Harris, P. Howitt, and J. Vickers (2001). Competition, Imitation and Growth with
Step-by-Step Innovation. The Review of Economic Studies, 68, 467-492.
Aghion, P. and P. Howitt (1998). Endogenous Growth Theory. Cambridge, MA: MIT Press.
Aghion, P., P. Howitt and D. Mayer-Foulkes (2005). The effect of financial development on convergence: Theory and evidence. Quarterly Journal of Economics 120, 173-222.
Akcigit, U., H. Alp and M. Peters (2021). Lack of selection and limits to delegation: Firm dynamics
in developing countries. American Economic Review, 111(1), 231-75.
Andersson, M. and S. Klepper (2013). Characteristics and performance of new firms and spinoffs
in Sweden. Industrial and Corporate Change 22(1), 245−280.
Baslandze, S. (2022). Entrepreneurship through employee mobility, innovation, and growth. Working Paper 2022-10, Federal Reserve Bank of Atlanta.
Baumol, W. J. (2002). The free-market innovation machine: Analyzing the growth miracle ofcapitalism. Princeton University Press.
Bernardt, Y., Kerste, R. A. J., and J. Meijaard (2002). Spin-off start-ups in the Netherlands: atfirst glance. EIM, Business and Policy Research.
Boldrin, M. and D. K. Levine (2013). The case against patents. Journal of Economic Perspectives
27(1), 3−22.
Brittain, J. W. and J. Freeman (1986). Entrepreneurship in the semiconductor industry. Mimeo,
University of Utah.
48

Cabral, L., Z. Wang and D. Y. Xu (2018). Competitors, complementors, parents and places:
Explaining regional agglomeration in the U.S. Auto Industry. Review of Economic Dynamics 30,
1−29.
Carmignani, A. and S. Giacomelli (2009). Italian civil justice: regional disparities. Bank of Italy
Occasional Paper 40, Bank of Italy, Rome.
Caselli, F. and N. Gennaioli (2013) Dynastic management. Economic Inquiry 51, 971−996.
Cavenaile, L., M. A. Celik and X. Tian (2020). Are Markups Too High? Competition, Strategic
Innovation, and Industry Dynamics, SSRN Working Paper.
Celik, M. A. and X. Tian (2018). Agency frictions, managerial compensation, and disruptive
innovations. Working paper, University of Toronto.
Chatterjee, S. and E. Rossi-Hansberg (2012). Spinoffs and the market for ideas. International
Economic Review 53(1), 53−93.
Chenevert, R., and M. G. Hoffman (2017). Common Pay Patterns and Extra Earnings: 2013,
Current Population Reports, P70BR150, U.S. Census Bureau, Washington, DC.
Christensen, C. M. (1993). The rigid disk drive industry: A history of commercial and technologicalturbulence. Business History Review 67(4), 531−588.
Cooley, T. F., R. Marimon and V. Quadrini (2014). Risky investments with limited commitment.
Working paper, University of Southern California.
Cooley, T. F. and V. Quadrini (2001). Financial markets and firm dynamics. American Economic
Review 5, 1286−1310.
Edmans, A., and X. Gabaix (2016). Executive compensation: A modern primer. Journal of Economic Literature, 54(4), 1232-1287.
Eriksson, T. and J. M. Kuhn (2006). Firm spin-offs in Denmark 1981–2000—patterns of entry andexit. International Journal of Industrial Organization 24(5), 1021−1040.
Etro, F. (2009). Endogenous Market Structures and the Macroeconomy, Berlin and New York:
Springer.
Franco, A. M. and D. Filson (2006). Spin-outs: knowledge diffusion through employee mobility.
Rand Journal of Economics 37(4), 841−860.
Garcia-Macia, D., Hsieh C.-T., and P. J. Klenow (2019). How destructive is innovation? Econometrica 87(5), 1507-1541.
Garvin, D. A. (1983). Spin-offs and the new firm formation process. California Management Review
25(2), 3−20.
Glover, B., and O. Levine (2017). Idiosyncratic risk and the manager. Journal of Financial Economics, 126(2), 320-341.
Hellmann, T. and V. Thiele (2011). Incentives and innovation: A multitasking approach. American
Economic Journal: Microeconomics 3(1), 78−128.
49

Howitt, P. (1999). Steady endogenous growth with population and R&D inputs growing. Journalof Political Economy 107, 715−730.
Hvide, H. K. (2009). The quality of entrepreneurs. Economic Journal 119(539), 1010−1035.
Iacopetta, M., Minetti, R. and P. F. Peretto (2019). Financial markets, industry dynamics andgrowth. Economic Journal 129(621), 2192−2215.
Iacopetta, M., and P. F. Peretto (2021). Corporate governance and industrialization. European
Economic Review, 135, 103718.
Jaumandreu, J. (2022). The Remarkable Stability of the US Manufacturing Markups. Working
Paper, Boston University.
Jones, C. I. and J. C. Williams (1998). Measuring the social return to R&D. Quarterly Journal of
Economics 113(4), 1119−1135.
Klepper, S. (2002). The capabilities of new firms and the evolution of the US automobile industry.
Industrial and Corporate Change 11(4), 645−666.
Klepper, S., & Sleeper, S. (2005). Entry by spinoffs. Management Science 51(8), 1291−1306.
Lee, Y. and T. Mukoyama (2015). Entry and exit of manufacturing plants over the business cycle.
European Economic Review 77, 20−27.
Moore, G. and K. Davis (2004). Learning the silicon valley way. Building high-tech clusters: Silicon
Valley and beyond, 7−39.
Muendler, M.-A. and J. E. Rauch (2018). Do employee spinoffs learn markets from their parents?
Evidence from international trade. European Economic Review 105(C), 159−173.
Muendler, M. A., Rauch, J. E. and O. Tocoian (2012). Employee spinoffs and other entrants:
Stylized facts from Brazil. International Journal of Industrial Organization 30(5), 447−458.
Nikolov, B. and T. M. Whited (2014). Agency conflicts and cash: Estimates from a dynamic
model. Journal of Finance 69(5), 1883-1921.
Peretto, P. F. (1999). Cost reduction, entry, and the interdependence of market structure andeconomic growth. Journal of Monetary Economics, 43(1), 173-195.
Pezone, V. (2021). The Real Effects of Judicial Enforcement. SAFE Working Paper No. 192.
Rocha, V., Carneiro, A. and C. A. Varum (2015). Entry and exit dynamics of nascent businessowners. Small Business Economics 45(1), 63−84.
Samaniego, R. M. and J. Y. Sun (2019). Rule of law, economic structure and development. Working
paper, George Washington University.
Sohail, F. (2021). From employee to entrepreneur: Learning, employer size, and spinout dynamics.
Journal of Economic Dynamics and Control 133, 104270.
Stiglitz, J. E. (2014). Intellectual property rights, the pool of knowledge, and innovation. NBER
Working Paper 20014.
50

Terry, S. J. (2017). The macro impact of short-termism. Working paper, Boston University.
Thompson, P. and S. Klepper (2005). Spinoff entry in high-tech industries: Motives and consequences. In Perspectives on Innovation, F. Malerba and S. Brusoni, eds., pp. 187−218, Cambridge
University Press.
Young, A. (1998). Growth without scale effects. Journal of Political Economy 106, 41−63.
World Bank (2013). Doing Business 2012. Doing Business in a More Transparent World. World
Bank. International Finance, Washington, DC.

51

52

0.058*(0.034)

Trial length

(3)

(4)

412
0.076

515
0.102

Y
N
Y
N
Y

-0.071***(0.025)Spinoff
intensity

78
0.169

Y
N
Y
N
N

0.050**(0.020)Spinoff
intensity

78
0.094

Y
N
Y
N
N

-0.013(0.275)Spinoff
intensity

(6)

(7)

(8)

17,000
0.045

Y
Y
Y
Y
Y

-0.091***(0.022)Innovation
probability

8,222
0.066

Y
Y
Y
Y
Y

-0.002**(0.001)

R&D/
Sales

17,000
0.045

Y
Y
Y
Y
Y

0.112***(0.033)Innovation
probability

8,222
0.065

Y
Y
Y
Y
Y

-0.001(0.002)

R&D/
Sales

Panel B: Judiciary and In-house Innovation

(5)

(10)

(11)

(12)

16,904
0.023

Y
Y
Y
Y
Y

-0.002(0.007)

Consensual
spinoff

16,904
0.023

Y
Y
Y
Y
Y

-0.000(0.007)

Consensual
spinoff

196
0.191

Y
Y
Y
N
N

-0.309*(0.179)Consensual
spinoff/
Total spinoff

196
0.185

Y
Y
Y
N
N

0.476*(0.243)

Consensual
spinoff/
Total spinoff

Panel C: Consensual and Conflictual Spinoffs(9)

Note: Panel A of this table reports OLS estimates for the effects of court (in)efficiency on the share of startups accounted for by spinoffs in a province;
Panel B reports OLS estimates for the effects of court (in)efficiency on the likelihood of innovation aimed at product improvement and on the amount of
R&D expenditure over sales; Panel C reports OLS estimates for the effects of court (in)efficiency on the likelihood of consensual spinoffs and on the ratioof consensual over non-consensual spinoffs in a province. All the regressions control for: (log) unemployment rate; population growth rate; bank branches
density; bank concentration (HHI); (log) trade openness; (log) material infrastructures; geographical area dummies; and time dummies. The regressions incolumns (5)-(10) control also for firm specific variables: total assets; firms’ age; ROE; and for 2-digit NACE industry dummies. Detailed definitions of thevariables are in Table A1. The table reports the regression coefficients and, in parentheses, the associated standard errors (clustered by province). *, **,*** denote statistical significance at the 10%, 5% and 1% level, respectively.

Observations
R-squared+ Provincial controls+ Firm-level controls
Area dummies
Sectoral dummies
Time dummies

Delta clearance rate (2012-2014)

Delta trial length (2012-2014)

Y
N
Y
N
Y

Spinoff
intensity

VARIABLES

Clearance rate

(2)

Panel A: Judiciary and Spinoffs(1)

Table 1: Quality of Judiciary, Spinoff Intensity, and Firm Innovation

Table 2: Parameters and Steady State

α
θ
λ

σ
ϕ
ρ
β

bJ
δJ
bˆJ
δˆJ
e

Panel A: External Parameters, Macroeconomy
Return Elasticity to Knowledge 0.300
Inverse Price Markup
0.790
Population Growth
0.012
Panel B: Targeted Parameters, Macroeconomy
Parameters
Targets
Variety Externality
0.25
nRate of Entry
Operating Cost
0.33
gG/L Output Growth Rate
Discount Rate
0.03
rReal Interest Rate
Entry Cost
2.34
s
Saving Rate
Panel C: Agency Relationships
Spinoffs
Parameters
Targets
Curvature
Scale up Factor
Curvature
Scale up Factor
Manager’s Efficiency

0.18
0.10
0.06
0.01
1.40
R&D

Σ
SJ
a
ωJ
RC

ParametersbI
δIhCurvature
Scale up Factor
Manager’s Efficiency

Spinoff Surplus
Diversion
Investment Rate
Compensation
Conflictual/Non Confl.

0.016
0.020
0.050
0.108

1.001
0.080
0.050
0.250
0.223

Targets
0.95
0.90
1.48

q
SI
ωI

Cost of Capital
DiversionCompensation

0.987
0.080
0.250

Panel D: Steady State
Macroeconomyzxc = C/Y

Firm Growth Rate
Firm Size
Consumption/Output

Governance
0.016
1.873
0.236

53

RS
Rm

Spinoff Ratio
Managers Compensation
over Assets

0.255
0.002

Table 3: Strengthening of Investment Protection (SI and/or SJ drop by half)

a
0

SJ
0

ωJ
0

ImpactLong Runz
0.01730.0256n-0.00290r
0.01690.0256RC-7.0133-6.9888

RS-0.93317

Rm
0.02060.0211Panel B: ∆δJ =0.0339
Governance
SI
ωI
0.2
-0.1609
Macroeconomy
x
c
s
r
0
0
0
-0.0009-0.04590-0.0092 -0.0047

RC-4.8699-4.8915RS
0.251
0.2429Rm
0.00320.0032

Panel C: ∆δI =0.0044 and ∆δJ =0.0339 (joint)
Governance
ωJ
SI
ωI
4.3
-3.8
2.5881
Macroeconomy
n
x
c
s
r
RC
-0.0016000
0.016-11.552
00.061500.01230.021-11.539RS-0.7-0.7118

Rm
0.02480.0248a0.0432SJ-4.0042ωJ
4.3

ImpactLong Runz-0.0011-0.0047n0.00120a0.0432SJ-4.0042

ImpactLong Runz
0.0163
0.021

Panel A: ∆δI =0.0044
GovernanceSIωI-4
2.7366
Macroeconomy
x
c
s
0
0
0
0.106900.0213

Note: Percentage points variations; changes of x multiplied by 100.

Figure 1: Comparative Steady State and Variations in R&D Investment Protection
Firm Growth, z

SSpinoffs Investment, aSpinoffs/Total Entry, R

0.018

2

0.4

0.016

0

0.3

0.014
0.885 0.89 0.895 0.9
Diversion, S

0.905

I

−2
0.885 0.89 0.895 0.9
Diversion, S

0.905

0.2
0.885 0.89 0.895

0.2

2

0.4

0.1

0

0.2

0
0.885 0.89 0.895 0.9 0.905
Compensation, ωI−20.885 0.89 0.895 0.9 0.905
Compensation, ωJ

00.885 0.89 0.895

0.4

2

3

0.2

0

0
0.885 0.89 0.895

0.9

0.905

Cost of Knowledge, q1−20.885 0.89 0.895 0.9
Surplus, Σ
4

0.9

0.905

Confl/ Non−Confl, RC

J

0.905

0.9

−3

0.905
m

Compens./Assets, R
x 10

1
0.885 0.89 0.895 0.9log(δJ/δI)

0.905

0

0.98

2

−0.02

0.96
0.885 0.89 0.895 0.9 0.905
Investor Protection, δI

0−0.04
0.885 0.89 0.895 0.9 0.905 0.885 0.89 0.895 0.9 0.905
Investor Protection, δI
Investor Protection, δI

54Figure 2: Comparative Steady State and Variations in Spinoff Investment Protection
Firm Growth, zSpinoffs/Total Entry, RS

Spinoffs Investment, a

0.016

0.05040.26

0.01590.05020.255

0.0159
0.1

0.11
0.12
Diversion, SI

0.13

0.085

0.08
0.1

0.11
0.12
Compensation, ωI

0.13

0.252

0.05
0.1

0.11
0.12
Diversion, SJ

0.13

0.08

0.25

0.06

0.2

0.04
0.1

0.11
0.12
Compensation, ωJ

0.13

0.3
0.28

0.25

0.25
0.1

0.11
0.12
0.13
Confl/ Non−Confl, RC

0.1
0.11
0.12
0.13
−3
Ratio, Rm
x Compensation
10
1.85
1.8

0.26
0.248
0.1
0.9868

0.11
0.12
0.13
0.1
Cost of Knowledge, q
1.00320.9866
0.9864
0.1

0.11
0.12
Surplus, Σ

0.13

1.75
0.1

0.11
0.12
log(δ /δ )

0.13

J I

0.4

1.003

0.2

1.0028
0.11
0.12
0.13
0.1
0.11
0.12
0.13
Investor Protection, δJ
Investor Protection, δ

0
0.1

J

0.11
0.12
0.13
Investor Protection, δJFigure 3: Comparative Steady State, Variations in Investment Protection for R&D and Spinoffs
Firm Growth, zSpinoffs/Total Entry, RS

Spinoffs Investment, a

0.018

0.05

0.4

0.016

0.05

0.3

0.014
0.885 0.89 0.895 0.9
Diversion, SI

0.905

0.05
0.885 0.89 0.895 0.9
Diversion, SJ

0.905

0.2
0.885 0.89 0.895

0.2

0.085

0.4

0.1

0.08

0.2

0.075
0
0.885 0.89 0.895 0.9 0.905 0.885 0.89 0.895 0.9 0.905
Compensation, ωI
Compensation, ωJ

00.885 0.89 0.895

0.4

0.252

3

0.2

0.25

2

0
0.885 0.89 0.895
1

0.248
0.905 0.885 0.89 0.895 0.9
Surplus, ΣCost of Knowledge, q
1.003

0.98

1.00290.9

0.96
0.885 0.89 0.895 0.9 0.905
Investor Protection, δI

0.9

0.905

Confl/ Non−Confl, RC

0.905

0.9

−3

0.905
m

Compens./Assets, R
x 10

1
0.885 0.89 0.895 0.9log(δJ/δI)

0.905

1
0

0.885 0.89 0.895 0.9 0.905
Investor Protection, δI

55−10.885 0.89 0.895 0.9 0.905
Investor Protection, δI

Figure 4: Policy Functions
Panel A: Shock to δI
Firm Growth, z

Total Entry, n0.01630.01610.01620.01610.01610.0162Pre shocksteady state

0.01610.01610.01610.01610.01610.016

0.0161

0.016

Pre shocksteady state

0.01591.87251.873

1.87350.01611.874

1.87451.8725Spinoffs/Total Entry, RS

1.873

1.87351.874

1.8745

Confl/ Non-Confl, RC

0.258
0.24

0.256
0.254

Pre shocksteady state

0.22

0.252

0.2

0.25

0.18

0.248
0.246
0.244
1.8725Pre shocksteady state

0.16
1.873

1.87351.874

1.87451.8725

Firm size, x

1.873

1.87351.874

1.8745

Firm size, xPanel B: Shock to δJ
Firm Growth, z

Total Entry, n

0.016

0.0161Pre shocksteady state

0.016

0.01610.016
0.01590.01610.01590.0161Pre shocksteady state

0.01591.8722

1.8724

1.87261.87281.873

0.0161
1.87220.17430.257

0.17420.25650.17420.256

0.17410.255
0.25451.8722

1.87261.87281.873

0.1741Pre shocksteady state

1.8724

1.8726

Confl/ Non-Confl, RC

Spinoffs/Total Entry, RS
0.25750.25551.87240.174
1.87280.174
1.8722

1.873

Pre shocksteady state
1.8724

1.87261.8728

1.873

Firm size, x

Firm size, xNote: In Panel A the shock to δI reduces SI by half; in Panel B the shock to δJ
reduces SJ by half. See also Table 3.

56

Figure 5: Joint Shock to δI and δJ (Policy Functions)
Total Entry, nFirm Growth, z
0.01630.01610.01620.01610.0162Pre shocksteady state

0.01610.01610.0161

0.016
0.016

Pre shocksteady state

0.01591.87281.873

0.01611.8732

1.87341.87360.0161
1.8728Spinoffs/Total Entry, RS
0.24

0.256

0.22

0.252

1.8732

1.87341.8736

Confl/ Non-Confl, RC

0.258

0.254

1.873

0.2
Pre shocksteady state

Pre shocksteady state

0.18
0.16

0.25

0.14

0.248

0.12

0.246
1.87281.873

1.8732

1.87340.1
1.87281.8736

1.873

Firm size, x1.8732

1.87341.8736

Firm size, xNote: In this figure the joint shock to δI and δJ reduces both SI and SJ by half.

Figure 6: Time Series
Firm Growth, zSpinoffs/Total Entry, R

0.01640.27

0.0162

0.26

0.016

0.25

0.0158

0

50

100

150

200

0.24

0

50

100

150

S

200

Confl/ Non-Confl, RC

Total Entry, n0.01620.25

0.01620.2

0.01610.15

0.0161
0

50

100

150

0.1

200

0

50

Per Capita GDP growth

100

150

200

150

200

Saving Rate

0.0204

0.10850.10840.02020.10830.02
0.01980.1082
0

50

100

150

200

0.1081Time (Years)

0

50

100

Time (Years)

Note: Time series for the shock to δJ (continuous lines), to δI (dashed lines), and joint shock (light colored lines). See
also Table 3.

57

Figure 7: Welfare Analysis(a) R&D (shock to δI )−40

Log diff Nx 10(b) Spinoffs (shock to δJ )−4

Log diff Z

0.03
0.02

2

−0.0041.5−0.006

1

−6

0.01

0

50

100

150

200

0

0

0

50

0.04

−0.0370.03−0.0375

0.02−0.0380.01−0.03850−0.039050

100Time (Years)

100

150

200

150

200

−4

50

150

200

−0.01

0

50

−3

9.9

100

150

200

150

200

Log diff Spinoffs

x 10

9.8

−0.0029.7−0.0049.6−0.0069.5
9.4−0.008
−0.01

0

50

Log diff N

100
Time (Years)

150

200

9.3
0

50

100
Time (Years)

150

200

9.2

0

50

100
Time (Years)(d) Subsidy (shock to τ ) and R&D (shock to δI )Log diff N

Log diff Z

0

100

0

(c) R&D and Spinoffs (shock to δI and δJ )
x 10

0

χ

Log diff Spinoffs−0.0365−0.008

0.5

χ
0.05

1

−0.002

2.5

−4

−8

Log diff Z
0

3

0.04

−2

Log diff N

x 10

3.5

0.05

0.05

0.01

0.04

0.008

0.03

0.006

0.02

0.004

0.01

0.002

−1

Log diff Z
0

−0.05

−0.1

−2
−3
−4
−5

0

50

100

150

200

0

0

0

50

100

150

200

−0.0278−0.028

0.04

0

50

100

150

200

−0.2

0

50

100

150

200

150

200

Log diff Spinoffsχ

Log diff Spinoffs

χ
0.05

−0.15

0

0.15

−0.05

0.1

−0.1

0.05

−0.15

0

−0.0282
0.03−0.0284
0.02−0.02860.01
0−0.0288050

100Time (Years)

150

200

−0.029

−0.2

0

50

100
Time (Years)

150

200

0

50

100
Time (Years)

150

200

−0.05

0

50

100
Time (Years)

Note: Each panel plots, going clock-wise from the top, log(Ñ /N ), log(Z̃/Z), log(Ñ S /N S ), and the consumption equivalent welfare variable χ. In Panel D continuous lines refer to the subsidy shock and dashed lines to a reduction of δI .

58

Figure 8: Welfare Sensitivity Analysis
Panel A: R&D (shock to δI )−30

Log diff N

x 10

Log diff Z
0.05

−0.2

0.04

−0.4

0.03

−0.6

0.02

−0.8

0.01

−1

0

50

100

150

0

200

0

50

100

150

200

150

200

150

200

150

200

Log diff Spinoffs

χ
0.05

0

0.04

−0.01

0.03
−0.02
0.02
−0.03

0.01
0

0

50

100
Time (Years)

150

200

−0.04

0

50

100
Time (Years)

Panel B: Spinoffs (shock to δJ )−4

8

Log diff N

x 10

Log diff Z
06−0.005
4
−0.01
2

0

0

50

100

150

200

−0.01510−0.0029.5−0.0049−0.0068.5−0.008

8

−0.01

7.5

−0.0127−0.014

6.5

50

100
Time (Years)

50

−3

χ
0

0

0

150

200

Log diff Spinoffs

x 10

0

100

50

100
Time (Years)

Note: See note of Figure (7). The continuous lines refer to an economy with a valueof e higher by 20% than the benchmark economy.

59

ONLINE APPENDIX“Growing through Spinoffs. Corporate Governance, Entry, and Innovation”

This Online Appendix contains more details on data and data sources (A1), proofs omitted fromthe main text (A2), further details on the model extension with subsidies of Section 8 (A3), a
discussion of the first best allocation (A4), and appendix tables and figures (A5).

A1. More Details on Data and Data Sources
Our main data sources are: the “Monitoraggio Economia e Territorio” (MET) survey of Italianfirms, and the “Indagine sulle Imprese Manifatturiere”, run by the Italian banking group UniCredit. We complement these main data sources with data of ISTAT on institutional and economic
characteristics of the provinces; Bank of Italy data on the structure of Italian provincial banking
sectors; and data of the Institute Guglielmo Tagliacarne for the quality of material infrastructuresin the provinces.
The data regarding spinoffs are sourced from the MET survey, which surveys approximately
25,000 firms per wave, encompassing both corporations and partnerships. The sampling design aims
to ensure representativeness at the industry, size, and region levels while also enabling oversamplingof specific features of interest. In contrast to other Italian and European data sets, this survey
includes information on firms of all sizes, including very small firms with fewer than ten employees.
The key advantage of this survey is its coverage of a broad range of micro (1-9 employees) andsmall firms (10-49 employees) which have a critical role in the Italian economy and represent asignificant share of Italian businesses. The MET survey is the largest firm survey conducted in asingle European country.
Information on the share of innovative firms and on the share of firms with R&D investments inthe provinces comes from the UniCredit survey. The UniCredit survey, which targets manufacturingfirms within Italy, includes a representative sample of manufacturing firms with 10-500 employees(about 94% of the firms in the sample) and the universe of manufacturing firms with more than
500 employees (approximately 5,000 firms were interviewed in the survey). The firms in the survey
1represent about 9% of the population in terms of employees and 10% in terms of value added.
Collected data include details about balance sheets, product and process innovation, R&D and other
innovation variables, company characteristics and demographics, sources of finance, relationships
with banks and mechanisms of information acquisition by banks. The surveyed firms have been inbusiness on average for 22 years; 60% of them have fewer than 50 employees (less than 4% havemore than 500 employees); 71% are based in the North. Only 1% are listed on the Stock Exchange,
while 37% have balance sheets certified by external auditors.

A2. Proofs

A2.1 Founder’s problem and Proposition 1
The Current Value Hamiltonian for the founder’s problem is

CV Hi = [(1 − ai ) + (e − ωJi )(1 − SJi )ai ][Πi − Ii − ωIi h(1 − SIi )Ii ] + qi Żisubject to the three managers’ reaction functions (12), (15), and (16), the one-to-one production
technology for the intermediate good, and the R&D technology Żi = h(1 − SIi )Ii ; qi is the shadow
value of knowledge accumulation. The first order conditions for ωIi and ωJi are−∂SI (ωIi ; δI , h, e)(qi − ωIi ) = 1 − SIi ,
∂ωIi

(A1)

and
(e − ωJi )(−∂a(ωJi ; δ̂J , e)
∂SJ (ωJi ; δJ , e))ai + [(e − ωJi )(1 − SJi )]= (1 − SJi )ai
∂ωJi
∂ωJi

(A2)

where the reaction functions SI (ωIi ; δI , h, e), SJ (ωJi ; δJ , e), a(ωJi ; δ̂J , e) are derived from (12), (15),and (16). The optimal condition for in-house investment, Ii , is(1 − ai ) + (e − ωJi )(1 − SJi )ai + ωIi h(1 − SIi ) = qi h(1 − SIi ).

2

(A3)

The combination of (A1) and (A3) gives(1 − SIi )2 h = [(1 − ai ) + (e − ωJi )(1 − SJi )ai ](−∂SI (ωIi ; δI , h, e)).
∂ωIi

The system consisting of the managers’ and the founders’ conditions (12), (15) and (16), (A1)(A2), yield the solution (SIi , SJi , ai , ωIi , ωJi ). Such solution is time-invariant. It is also firminvariant. Therefore, we drop the subscript i from the solution of the governance variables. We
also drop the subscript i from qi as this is also firm-invariant. Furthermore, q is constant: q̇ = 0.
Turning to the state variable, Zi , we have that

r=

α Πi
,
q Zi

(A5)

Σ
where q = h(1−S+ ωI , with Σ ≡ (1 − a) + (e − ωJ )a(1 − SJ ) (see A3). Equation (A5) correspondsI)

to equation (25).
Resource Constraint and Household Budget Constraint
We show that the households’ budget constraint reported in (33) can be derived from theeconomy’s resource constraint. We have already argued that the governance solution is firminvariant. We also use the general equilibrium symmetry property and omit the subscript i fromthe firm’s variables. With these observations in mind, the economy’s budget constraint is

C + C m + N (X + ϕQ + (1 − SI )I + ṄF

βX
βX
βX
+ ṄI
+ ṄJ
) + Ṅ ex−novo βX = Y,eeewhere on the left hand side we have, starting from the left, the households’ consumption, themanagers’ consumption C m , the production cost of intermediate firms, the outlays for spinoffs
and the startup cost of ex-novo firms. Using (31), (8), (9), and (10) and the definition F =Π − I − ωI h(1 − SI )I, after some algebra, the above expression becomes
C+N {[ωJ e(1−SJ )a+eSJ a]F +ωI h(1−SI )I+eSI I}+N [X+ϕQ+I+(1−SF )aF +SF aF ]+Ṅ ex−novo βX = Y .

3

Making explicit the spinoff creation associated with resource diversion, we have

C+N [ωJ e(1−SJ )aF +ωI h(1−SI )I]+ṄI βX+ṄJ βX+N [X+ϕQ+I+(1−SF )aF +SF aF ]+Ṅ ex−novo βX = Y .

Using (5) the above relationship becomes
C + N [ωJ e(1 − SJ )a]F + ωI h(1 − SI )I] + N [ṄI βX + N ṄJ βX]+
+N (X + ϕQ + I + (1 − SF )aF + SF aF ) + Ṅ ex−novo βX = wL + N P X.
Subtracting variable and operating costs and investment outlays from both sides, after some algebra,
we obtain

C + N (ṄI βX + ṄJ βX) + Ṅ ex−novo βX = wL + N F (1 − a) − ωJ e(1 − SJ )N aF.
Adding N ṄF βX to both sides, and noticing that Ṅ = Ṅ ex−novo + N (ṄI + ṄJ + ṄF ), and thatβX = V , we have
C + Ṅ V = wL + N [(1 − a) + (e − ωJ )(1 − SJ )a]F
which, using (20), reduces to
C + Ṅ V = wL + N Γ.
Finally, combining this result with the entry arbitrage condition r = VΓ + V̇V , the observation thatA = N V and that Ȧ = Ṅ V + N V̇ , delivers the houshehold budget constraint in (33):Ȧ = wL + rA − C.

Proposition 2 (Steady State)
The solution for x and z eqs. (42) ad (43) is the steady state firm size, x∗ , and the in-house
investment rate, z ∗ . The steady state entry rate n∗ follows from setting ẋ = 0 in (46). The
real interest rate r∗ is computed through (37). The derivation of the expression for c∗ in (44) is
explained below.

4Proposition 3 (Dynamic System)
The free-entry condition implies that A = N βX. Using the factor payments property (5) wehave A = βθ2 Y and wL = (1 − θ)Y . These results combined with budget constraint (33) and theEuler equation (34) yieldċ
1= λ − ρ − 2 [(1 − θ) − c].
cβθThe unique solution of this unstable differential equation isc∗ = (1 − θ) − (λ − ρ)βθ2 .

We obtain the growth rate of the firm size in (46) by log differentiation with respect to time of
the two sides of (36).
We derive the entry rate, n(x), from the economy’s resource constraint. Specifically, startingfrom the result
C + Ṅ V = wL + N Γ(see section Resource Constraint and Household Budget Constraint of this Appendix), we use (20),(5), (7) and the free entry condition βX = V to obtainC + nβXN = (1 − θ)Y + [(1 − a) + (e − ωJ)a(1 − S)][N ((P − 1)X − ϕZ) − (1+ ωIi )zN Z].
h(1 − SI )Using the factor payment property N P X = θY and the definition x ≡ X
Z , we obtainϕ1z
C + nβθ2 Y = (1 − θ)Y + [(1 − a) + (e − ωJ)ai (1 − S)][θ2 Y ((P − 1) − ) − (+ ωI ) θ2 Y ].
xh(1 − SI )x
Finally, dividing both sides by βθ2 Y we getn = [(1 − a) + (e − ωJ )a(1 − SJ )][((P − 1) −ϕ1z 1
(1 − θ)1)−(+ ωI ) ] +− c 2,2xh(1 − SI )
x β
βθ
βθ

and, replacing c with its equilibrium solution (1 − θ) − (λ − ρ)βθ2 , we have the expression in (47),5

reported here for convenience:

n=

Σ 1
ϕ
1
z
[( − 1 − ) − (+ ωI ) ] + (λ − ρ)β θxh(1 − SI )xwith Σ ≡ (1 − a) + (e − ωJ )a(1 − SJ ).
Turning to z, we combine the two arbitrage conditions (38) and (37) and obtain


Σ 1
ϕ
1
z
ẋ
α 1
[( − 1) − − (+ ωI ) ] + + z = [− 1 x − ϕ]β θxh(1 − S)xx
q θΣ+ ωI . Using (47) and the definition of x in (36) the above
where we recall that q = h(1−SI)

expression becomesn − (λ − ρ) + (σ − 1)n + λ + z =



α 1
[
− 1 x − ϕ]q θthat is


α 1
z= [
− 1 x − ϕ] − σn − ρ.
q θFinally, this equation and (47), after some algebra, yield (48), that is


α σΣ
1
z(x) ={( −)[− 1 x − ϕ] − ρ(1 − σ) − σλ}.
1qβx
θ1 − σΣ
βx ( h(1−SI ) + ωI )1

The solution assumes a value of x sufficiently large that entry and in-house innovation are bothactive, that is, the equity and internal return, (38) and (37), are equal to the return on saving (34).
If rZ < r = rN , that is


α 1
Σ 1
[
− 1 x − ϕ] <[( − 1)x − ϕ] + (σ − 1)n + λq θ
βx θ
z = 0, and n > 0. We then assume that the initial condition x(0) > xZ where


α
Σ
1
arg solvexZ = {( − σ ) × [− 1 x − ϕ] = (1 − σ)ρ + σλ}
q
βx
θ

6

Similarly, equation (47) implies that a sufficient condition for n(x) > 0 is thatx(0) > xN ≡ϕβ( 1θ − 1) + (λ − ρ) Σ.

A3. Model Extension: Entry and R&D Subsidies
This section gives additional details on how key equations of the baseline model are to bemodified to incorporate the role of a government that collects a labor tax on labor τ wL and thatuses the proceeds to subsidize the creation of new firms or to subsidize R&D. The dynamic arbitrage
entry conditions (38) becomesr = rN ≡11ẋΣ[( − 1)x − ϕ − (+ ωI )z] + + z.
β(1 − γ)x θh(1 − SI )(1 + ξ)x(61)

The expressions for the return on innovation and on savings are the same as in (37) and (34),respectively, except that the expression for q now is:

q=

Σ
+ ωI
h(1 − SI )(1 + ξ)

Combining (61) with (37), (34), and (57), and with the aggregate resource constraint gives thefollowing dynamic system (when both entry and innovation are active)1ċ=[(1 − θ)(1 − τ ) − c] − (λ − ρ),cβ(1 − γ)θ2n = Σ[((P − 1) −ϕ
1z
1
(1 − θ)c)−(+ ωI ) ]+−2xh(1 − SI )(1 + ξ)
x β(1 − γ) β(1 − γ)θ
β(1 − γ)θ2


α 1
z= [
− 1 x − ϕ] − σn − ρ.
q θCombining the previous two equations we get:


1 α
σ
Σ
1
σ
z = {( −)[− 1 x − ϕ] − ρ −(1 − θ − c)},ψ qβ(1 − γ) xθβ(1 − γ)θ2
σΣwhere q = h(1−SΣ+ ωI , ψ ≡ 1 − β(1−γ)x( h(1−SI1)(1+ξ) + ωI ).
I )(1+ξ)7

The evolution of x is characterized by the expressions in (46) of Proposition 1, that for convenience we report here:ẋ= λ − (1 − σ)n(x).
xThe dynamics of this extended model differ from the baseline because the consumption-output
ratio, c, is no longer time-invariant as the tax rate τ in general depends on the firm size x. Also,
under an alternative tax-subsidy scheme in which the government fixes τ and allows the subsidy
γ to adjust, c would still depend on x through γ. In our quantitative experiments, however, theconsumption-output ratio, c, is nearly constant, that is the saddle path is nearly flat in the (x, c)space.

A4. First Best Allocation
Several features of the economy suggest that the decentralized equilibrium can be significantly
different from the planner’s allocation. Detailing a social planner solution is beyond the scope ofthe current analysis. Nevertheless, the welfare analysis already shed some light on what type ofgovernment interventions is more likely to approach the first best allocation. The decentralized
equilibrium is characterized by three kinds of inefficiencies. One source of inefficiency is the monopolistic price, which reduces the flow of intermediate products and hence of final output, given
the state of the technology. None of the reforms or policy interventions we considered corrects
for this distortion. Second, because each intermediate firm benefits from a knowledge externality,
the amount of R&D resources invested by incumbents is below that of the first best. The R&D
subsidies considered in the extended economy, which are financed through non-distortionary taxes,
partly correct such distortion. But also an improvement of the R&D governance (δI ) helps to getcloser to the first best. In other words, R&D agency issues tend to push the decentralized economy
away from the first best allocation. A third source of inefficiency can be excessive entry. On the
one hand a new firm tends to raise productivity through variety expansion. On the other hand
it consumes set up resources, and more importantly, it requires a continuous flow of resources for
its operation. A social planner may find it optimal not to allow entry if the social variety σ issufficiently small. Further, this would have the advantage of increasing the size of existing firms

8and thus increase the social return on R&D. However, if the social planner determines that it is
optimal to allow some entry (i.e. if σ is sufficiently high), it would privilege spinoffs, as these allow
a more efficient use of existing knowledge.

A5. Appendix Tables and Figures
Table A1: Variables Definitions and Data Summary Statistics
Variable
Main variables
Spinoff rate

Innovation probability

R&D/Sales
Consensual spinoff

Consensual/Total spinoffs
Trial length
Clearance rate
Other variables
Unemployment rate (log)
Population growth
Bank branches density
Bank concentration (HHI)
Trade openness (log)Material(log)

infrastructures

Definition

Mean

Std dev

Percentage ratio in province between spinoffs (firms
in which the founders of the firm were previously
managers or employees of incumbent firms operatingin the sector) and total entrants.
Dummy variable equal to one if the firm reports tohave realized innovations aimed at product improvement over the three years covered by the survey, zerootherwise.
R&D investments scaled by total sales.
Dummy variable equals to one if the firm reports
to have realized a consensual spinoff over the three
years covered by the survey, zero otherwise.
Ratio between consensual and total spinoff in theprovince.
Average duration of the trials in the courts locatedin the province.
Ratio between the total number of incoming cases by
the total number of outgoing cases.

0.248

0.324

0.640

0.482

0.041
0.035

0.019
0.184

0.753

0.618

0.630

0.226

0.829

0.224

2.030
0.003
0.547

0.587
0.005
0.193

0.101

0.054

-1.238

0.930

4.473

0.440

Logarithm of provincial unemployment rate.
Growth rate of population in province.
Number of bank branches in province, per 1,000 inhabitants.
Herfindahl-Hirschman index of bank branches inprovince.
Logarithm of ratio of trade to GDP in province in
2001.
Index (0-100) of material infrastructures in province.
Infrastructures: Road Network, Railways, Ports,
Airports, Environmental Energy Networks, Broadband Services, Business Structure.

Note: This table reports definitions and summary statistics for the variables used in the analysis.

9Table A2: Robustness and Further Sensitivity (δˆJ 50% higher)
Panel A: ∆δI =0.0044
Governance
SI
ωI
-4.3
2.774
Macroeconomy
x
c
s
r
0
0
0
0.01670.103400.0205 0.0251

RC-7.6707-7.6651

RS-0.9897-1.0104Rm
0.02090.0215

Panel B: ∆δJ =0.0339
GovernanceSIωI
0-0.0024
Macroeconomy
x
c
s
r
0
0
0
0
-0.01190-0.0024 -0.0011

RC-5.9119-5.9159RS
0.31040.3125Rm
0.0061
0.006

Panel C: ∆δI =0.0044 and ∆δJ =0.0339 (joint)
Governance
ωJ
SI
ωI
5.1
-4.3
2.7713Macroeconomy
n
x
c
s
r
RC
-0.00240000.0166 -13.1004
00.091600.0181
0.024-13.096RS-0.6787-0.6973Rm
0.02760.0281

a
0

SJ
0

ωJ
0

ImpactLong Runz
0.01710.0251n-0.00280a0.0665SJ-4.873

ωJ
5.1

ImpactLong Runz-0.0002-0.0011n0.00030a0.0665SJ-4.873

ImpactLong Runz
0.01690.024

Note: Percentage points variations; changes of x multiplied by 100.

Table A3: Robustness and Further Sensitivity (1 − α = 0.35)

a
0

SJ
0

ωJ
0

ImpactLong Runz
0.02290.0343n-0.00280a0.0432SJ-4.0042ωJ
4.3

ImpactLong Runz-0.0014-0.0067n0.00130Panel A: ∆δI =0.0044
GovernanceSIωI-4
2.7366
Macroeconomy
x
c
s
r
0
0
0
0.02260.1237
00.0217 0.0343

RC-13.6133-13.5875

RS-1.8478-1.8683Rm
0.0303
0.031

Panel B: ∆δJ =0.0339
Governance
SI
ωI
0.2
-0.1609
Macroeconomy
x
c
s
r
0
0
0
-0.0013-0.05780-0.0102 -0.0067

RC-5.1758-5.2005RS
0.28080.2894Rm
0.00280.0025

Note: Weaker knowledge externalities. In the baseline case (Table 3, Panels A and B) 1 − α = 0.7.

10

Table A4: Entry Subsidy and Easying Spinoffs

Panel A: ∆τ =0.1%
Governance
SI
ωI
0
0
Macroeconomy
x
c
s
r
0
-0.0233 0.0881 0.0001-1.2325 -0.0443 -0.0808 -0.0996

RC-0.0426-0.6431RS-0.2947-0.0886Rm
0.0014-0.0043

Panel B: ∆δI =-0.0156 (1.75% drop)
Governance
ωJ
SI
ωI
0
12
-9.7358
Macroeconomy
n
x
c
s
r
0.0113000-0.06560-0.42030-0.0842 -0.099

RC
23.870923.2896

RS
3.13273.1637Rm-0.0718-0.0729

a
0

SJ
0

ωJ
0

ImpactLong Runz-0.0045-0.0996n0.0337
0

a
0

SJ
0

ImpactLong Runz-0.0671-0.099Note: Percentage points variations; changes of x multiplied by 100.

Table A5: R&D Subsidy and Stronger R&D Investment Protection

a
0

SJ
0

ωJ
0

SI
0

ImpactLong Runz
0.01740.0256n-0.00290x
00.1062∆ξ=0.34%
GovernanceωI-0.0847
Macroeconomycs-0.0003 0.0011-0.0003 0.0223

r
0.017
0.0256

RC
0.11050.1612RS
0.04040.0223Rm
0.00030.0008

Note: Percentage points variations; changes of x multiplied by 100. Compare with the results in Table 3, Panel A.

11

Figure A1: Spinoffs, Innovation and Judiciary in Italy(a) Spinoff Rate(b) In-house Innovation Intensity(c) R&D Workers(d) Cost of Judiciary

Note: The maps display the spinoff rates drawn from the “Rilevazione sul Sistema delle Start-up Innovative”, a survey of start-ups carried out by the Italian Ministry of Economic Development (PanelA), innovation intensities sourced from the “Survey of Italian Manufacturing Firms” conducted by
the Italian banking group UniCredit (Panel B), the number of R&D workers over population using
data from the survey of innovative firms conducted by ISTAT (Panel C), and the cost of accessing
courts from ISTAT (Panel D) in Italian provinces (Panels A, B and D) or regions (Panel C). See
Section 3 for detailed definitions.

12Figure A2: Robustness Analysis

Firm Growth, z

−4

3

x 10

−3

−6.8

2.5

−7

2

−7.2

1.5

0

50

150

−7.4

200

x 10

−2

−0.13

50
−4

3

x 10

50

100

150

200

−0.11
−0.12

0

Spinoffs/Total Entry, RS

Confl/ Non−Confl, RC

−1

−3

0

Total Entry, n

−5

0

100

x 10

100

150

200

−0.14

Per Capita GDP growth

0

50

150

200

150

200

Saving Rate

−4

2

100

x 10

2.5
1
2
1.5

0

50

100

150

0

200

Time (Years)

0

50

100

Time (Years)

Note: Joint shock to δI and δJ . The continuous lines represent the response of the baseline economy. The dashedlines represent the response of an economy with δ̂J 50% higher than the baseline economy. They are differences withrespect to relevant pre-shock steady state values.

13Figure A3: Entry Subsidy (Policy Functions)Firm Growth, z

Total Entry, n

0.016
0.01580.0166Pre shock
Steady State

0.0165

Entry Subsidy

0.01560.0164
Entry Subsidy

0.0154
Investor Protection(lower δI)

0.01520.01630.01620.015
0.0148
1.86

Investor Protection(lower δI)

1.865

1.87

1.875

0.0161
1.86

Spinoffs/Total Entry, RS

1.865

1.87

1.875

Confl/ Non−Confl, RC

0.29

0.5
0.45

Investor Protection(lower δI)

0.28

Investor Protection(lower δI)

0.4

0.27

0.35
0.3

0.26

0.25
1.86

Entry Subsidy

1.865

1.87

0.25
1.875

Firm size, x

0.2
1.86

Entry Subsidy
1.865

1.87

Firm size, x14

1.875