(AGENPARL) – mar 23 luglio 2024 Mercati, infrastrutture, sistemi di pagamento
(Markets, Infrastructures, Payment Systems)
From Public to Internal Capital Markets:
The Effects of Affiliated IPOs on Group Firms
Number
July 2024
by Luana Zaccaria, Simone Narizzano, Francesco Savino and Antonio Scalia
Mercati, infrastrutture, sistemi di pagamento
(Markets, Infrastructures, Payment Systems)
From Public to Internal Capital Markets:
The Effects of Affiliated IPOs on Group Firms
by Luana Zaccaria, Simone Narizzano, Francesco Savino and Antonio Scalia
Number 49 – July 2024
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From Public to internal caPital markets:
the eFFects oF aFFiliated iPos on GrouP Firms
by Luana Zaccaria,* Simone Narizzano,** Francesco Savino** and Antonio Scalia**
Abstract
Using detailed data on corporate ownership for private and public firms, we document the effects
of group-affiliated initial public offerings (IPOs) on other unlisted fi rms in the same group. We
find evidence of a significant and persistent decrease in leverage (-6 per cent) and of an increase
in employment (+18 per cent), with the latter effect being more pronounced for more financially
constrained, younger, and smaller firms within the group. By comparing the determinants and the
ex-post effects of IPOs on affiliated and stand-alone issuers, we show that affiliated IPOs are less
likely to be driven by the investment needs of the issuer. Overall, this evidence is consistent with
the hypothesis that relaxing financial constraints and expanding the workforce in group firms are the
intended objectives of affiliated IPOs rather than side effects.
JEL Classification: G32.
Keywords: IPOs, Business Groups, Financial constraints.
Sintesi
Sfruttando dati dettagliati sulla struttura proprietaria delle imprese italiane, si valutano gli effetti
della quotazione di un’impresa appartenente a un gruppo sulle altre imprese (non quotate) dello
stesso gruppo. I risultati mostrano una riduzione significativa e persistente della leva fi nanziaria
(-6%) e un aumento dell’occupazione (+18%); quest’ultimo effetto è più pronunciato per le imprese
maggiormente vincolate dal punto di vista finanziario, più giovani e più piccole all’interno del
gruppo. Confrontando le determinanti e gli effetti ex post della quotazione sulle imprese affiliate e su
quelle autonome, si osserva che è meno probabile che la quotazione di un’affiliata sia guidata dalle
proprie esigenze di investimento. Nel complesso, questa evidenza è coerente con l’ipotesi secondo
cui l’allentamento dei vincoli finanziari e l’espansione della forza lavoro nelle imprese del gruppo
sono obiettivi specifici della quotazione delle affiliate piuttosto che “effetti collaterali”.
Einaudi Institute for Economics and Finance.
** Bank of Italy, Directorate General for Markets and Payment Systems.
CONTENTS
1. Introduction
2. Literature review
3. Data collection and descriptive statistics
4. The effects of affiliated IPOs on group firms
5. Group affiliation and the going-public decision
6. Robustness
7. Conclusions
References
Figures and tables
Introduction1
Over the last few decades, financial regulators in various countries have enacted reforms
that aim at expanding and facilitating access to public equity markets (Bernstein et al.
[2020]). These policies are based on the premise that well-developed public capital markets can foster growth and innovation to the benefit of the whole economy (see Levine
[2005]). Yet, the real effects of new public equity issuances, and in particular IPOs, on
firm-level and aggregate outcomes are contentious. Some studies find that IPOs play a
limited role in financing growth. For example, IPOs can serve the purpose of rebalancing the firm’s capital structure and lowering the cost of financing (Pagano et al. [1998]),
exploiting temporary fluctuations in market valuations (Baker and Wurgler [2002]), or
providing liquidity and diversification to early investors and founders (Bodnaruk et al.
[2008]). Other researchers suggest instead that the funds raised in IPOs are actually
used to finance investments in fixed assets and R&D (Kim and Weisbach [2008]). More
recently, Borisov et al. [2021] show that access to public equity markets also contributes
to employment growth.
In assessing the motivations and the effects of IPOs, most previous research implicitely treats newly listed companies as stand-alone entrepreneurial firms and, consequently, examines issuer-level outcomes.2 This approach is arguably too narrow when
the issuing firm belongs to a business group, which is a common occurrence, especially
in Europe and in emerging markets.3 An affiliated-IPO, i.e., the IPO of a firm that
We are grateful to Tommaso Perez, Francesco Columba, Francisco Urzua, Merih Sevilir, Giovanna
Nicodano, Sergey Tsyplakov and participants to the 2023 UBC Finance Summer Conference, Venice
Finance Workshop, 12th ICEF-CInSt International Moscow Finance Conference, Bank of Italy MISP
Seminar, USI Lugano, ASU Finance, and LUISS University for very useful comments and suggestions.
We also thank Patrizia Celia and Caterina Crociata for sharing Borsa Italiana’s data on IPOs.
Few papers in the spin-offs literature are notable exceptions, see Michaely and Shaw [1995] and
Dittmar [2004] for empirical contributions and Dai et al. [2020] for a theoretical one.
Larrain et al. [2021] estimate that 23% of European initial public offerings (IPOs) and and 45% of
the market capitalization of new issues since the year 2000 correspond to firms affiliated with business
groups.
belongs to a business group, is presumably part of a wider business strategy and its
effects may stretch beyond the issuer to all the firms comprising the group. In light of
the importance of this form of industrial organization around the world ( see Khanna
and Yafeh [2007]), in this paper we ask whether and to what extent affiliated-IPOs
affect investments in both fixed and human capital for other firms in the group.
The answer to this question is not immediately obvious. On the one hand, an
IPO is a costly and time-consuming process that may drain resources from the entire
organization, including firms that are not directly involved in the listing. On the other
hand, IPOs may allow the more mature firms in the group to raise new funds that
can be employed to relax other firms’ financial constraints. However, differently from
other forms of intra-group transfers (such as loans or dividens, see Buchuk et al. [2014]
and Gopalan et al. [2014]), cross-subsidizing firms with publicly issued equity involves
additional costs (e.g., in the form of legal and administrative fees, loss of control,
mandatory disclosures), making it unclear whether in practice affiliated-IPOs can play
a significant role in internal capital allocation within groups.
Our empirical investigation hinges on detailed ownership data for the universe of
Italian private and public firms, which allows us to map business groups, i.e., sets
of firms that share the same ultimate corporate owner. This dataset has two main
advantages. First and foremost, it allows us to identify private firms linked to IPO
firms through business group ties even when such connections could not be uncovered
using commonly available information. For example, suppose that company A, which
is partly owned by private company B, goes public, and that company B also owns
(or is a large shareholder of) company C. Companies A and C are connected through
the ultimate owner (company B), but most commercial datasets may not reveal this
link, for instance if company B is not subject to mandatory disclosures. Our data –
based on local administrative records – enables us to unearth the common ownership
of A and C, and therefore to correctly classify IPOs as stand-alone or affiliated even
when ultimate owners do not publicly disclose their holdings. In our sample, over 50%
of the IPOs are classified as affiliated. More importantly, we can track changes in C’s
outcomes following A’s IPO to examine the role of initial equity offerings in internal
capital markets. Second, the information on group affiliation for the universe of Italian
firms (not only those that eventually go public) can be used to construct accurate
control samples and reduce estimation biases. Firm-level financing and investment
policies may be drastically different between stand-alone and affiliated firms, implying
that affiliation status is a crucial (yet often omitted) factor to take into account when
comparing outcomes across firms. We compare outcomes of IPO-group firms (firm C
from the example above) around the IPO year to those of other affiliated firms (of
similar size and operating in the same sectors) belonging to groups that did not list any
of the member firms.
Our results show that, following an affiliated IPO, group firms increase their assets
base (+11%) and expand the labor force (+18%). These changes occur as one-off permanent increases in levels. Importantly, we document a significant drop in financial
leverage (-6%), which is not explained by changes in assets’ profitability or tangibility,
and does not bring savings in terms of the cost of debt. This suggests that affiliated
IPOs unlock fresh equity capital which is employed by group firms to finance labor.
Thus, our evidence is consistent with previous literature on the relationship between
firms’ employment decisions and financial leverage (see for example Agrawal and Matsa
[2013], Benmelech et al. [2021], Baghai et al. [2021]). In particular, Simintzi et al. [2015]
show that rigid labor claims generate operating leverage, especially in jurisdictions that
are more protective of workers’ rights, implying that large investments in human capital
may require a reduction in financial leverage. Moreover, unlike fixed capital, human
capital cannot be owned nor pleadged, making it less suitable for debt financing. In sup-
port of this interpretation, we show that the effect on employment is more pronounced
for more levered, younger, and smaller firms within the group, which is consistent with
the idea that the proceeds of affiliated IPOs contribute to relaxing financial constraints
of group firms. Assets of group firms increase more when existing (rather than new)
shares are sold in the IPO, and ownership becomes more concentrated post-listing,
suggesting that the effects that we document are directly related to the affiliated IPO
through the improved liquidity of the ultimate owners’ portfolio.
Having documented the effects of affiliated-IPOs on group firms, we address the
question of whether these effects are a simple by-product or can be interpreted as one
of the motivations for the affiliated IPO. Three pieces of evidence lend support to the
second view. First, affiliated firms are more likely to sell secondary shares (i.e., existing
shares) as compared to stand-alone firms (34% vs 14%). This suggests that by listing
one of the group companies, ultimate owners cash in (part of) their initial investment
in the IPO firm and collect liquidity which can be potentially invested in different
projects. Second, as documented by previous research, the going-public decision of
stand-alone firms correlates with firm leverage and with industry-specific market-tobook ratios. This is consistent with view that firms list their shares when they face
large investment opportunities which cannot be fully funded through other standard
sources (e.g. bank loans or trade credit) due to high levels of indebtness. Instead, we
show that affiliated IPOs do not present these empirical regularities, i.e., the probability
of going public is unrelated to leverage and industry market-to-book ratios, suggesting
a weaker correlation with the firm’s investment needs. Third, for each dollar of proceeds
generated by the sale of primary shares, the issuer’s assets increase by 3 dollars after
stand-alone IPOs, but only 1.5 dollars after affiliated IPOs. This is mainly due to the
fact that stand-alone firms complement new equity from the IPO with a significantly
larger amount of debt capital as compared to affiliated firms. Moreover, affiliated firms
are more likely to hold the IPO proceeds in cash or cash equivalent accounts rather
than investing them in working capital or fixed assets. Taken all together this evidence
is consistent with the view that affiliated firms are less likely to go public to raise
investment capital for their own projects.
Overall, our study shows that business groups can use public capital markets to feed
their internal capital markets. This implies the possibility of capital misallocation and
significant conflicts of interest between controlling and minority shareholders, especially
when corporate governance rules are lenient (Johnson et al. [2000]), an implication often
referred to as the “dark side” of internal capital markets. The “bright side” of internal
capital markets, however, is that diversification and intra-group transfers may lead to a
systematic overperformance of affiliated firms over stand-alone firms, due to lower cash
flow volatility (see Gopalan et al. [2007] and Boutin et al. [2013]). We examine monthly
stock returns of affiliated and stand-alone IPO firms in our sample, and, consistently
with both a dark and a bright side of internal capital markets, we find that affiliated
stocks generate an extra monthly return of approximately 90 basis point if listed on
the main exchange, but underperform by 70 basis points if listed on the “start-up”
segment, where regulatory requirements in terms of governance are significantly less
stringent.4 Said differently, affiliated firms’ valuations account for the implicit cashflow insurance provided by the group and, consequently, are larger than stand-alone
firms’, but only provided that governance practices are in place to avoid expropriation
of minority shareholders.
The rest of the paper proceeds as follows. Section 2 reviews the literature and
Section 3 describes the data construction process and the sample descriptive statistics.
We examine the effects of affiliated IPOs on group firms in Section 4, and investigate
the differences in the going public decision between affiliated and stand-alone firms in
In a recent study, Faccio et al. [2021] show that investors expectations of resource and risk reallocation within groups reduce group firms’ idiosyncratic stock return volatility from commodity shocks.
Section 5. In Section 6 we present the results of a series of robustness tests. Section 7
concludes.
Literature Review
This study relates to two main strands of literature. The first is the large body of
empirical and theoretical finance research on the decision to go public and its effects on
firm financing and investment policies. Within this strand, previous studies have investigated the role of IPOs in firms’ capital structure and expenditure decisions (Pagano
et al. [1998], Lowry [2003], Kim and Weisbach [2008]), innovation (Bernstein [2015])
and, more recently, organization and employment (Borisov et al. [2021], Babina et al.
[2022], Bias et al. [2022]). Focusing on direct firm-level outcomes, this research offers
mixed evidence broadly consistent with two views. The first is that IPOs relax financial
constraints for the issuing firm allowing for investment both in fixed and human capital
(albeit bringing changes in governance that can affect employee incentives and the allocation of the workforce). The second is that IPOs do not have an effect on investments
(at least not directly) as firms go public to exploit market sentiment (possibly at the
expense of new shareholders) and rebalance their capital structure, thus lowering the
firm’s cost of funding and increasing the liquidity of the existing shareholders’ wealth.
As in previous studies, we examine these two hypothesis but we expand the analysis
to firms that belong to the same “strategic nexus” (i.e., business group) as the issuer.
In this sense, this paper also relates to a smaller strand of literature that examines
the indirect (or spillover) effects of IPOs on trade partners (Kutsuna et al. [2016]),
competitors (Spiegel and Tookes [2020], Aghamolla and Thakor [2022]), and the local
economy (Butler et al. [2019]). Differently from this literature, we do not treat IPOs
as exogenous events, rather we find evidence that places the going-public decision of
affiliated firms within a broader group strategy.
Secondly, our study directly relates to the literature on business groups and internal
capital markets. Business groups, consisting of legally independent firms linked by
ownership ties, are very common forms of industrial organizations, especially in the
emerging markets, but also in developed countries (e.g., Italy and Sweden).5 One of
the benefits of business groups is the presence of internal markets through which capital
can be allocated among member firms, especially when local financial markets are less
developed (see for example Masulis et al. [2011]).6 Indeed, previous research documents
that internal capital markets can mitigate the effect of economic and financial crises (see
Almeida et al. [2015], Santioni et al. [2020]) and support investments in new projects or
products (Boutin et al. [2013]), particularly when they are capital intensive and require
high-skill labor (Bena and Ortiz-Molina [2013]).7 The transfer of resources within the
internal capital market has been shown to occur through intra-group loans (Buchuk
et al. [2014]) or dividend policies (Gopalan et al. [2014]), but extant literature is thus
far silent on the role of affiliated IPOs in shifting resources across group firms. On
the one hand, issuing new public equity may not be optimal for such transfers since,
differently from loans and dividends, it involves additional costs, including those related
to loss of control (Brau and Fawcett [2006]), disclosure requirements (Farre-Mensa
[2017]; Aghamolla and Thakor [2022]), takeover risk (Zingales [1995]), and short-termist
pressures (Asker et al. [2015]). On the other hand, public equity markets allow for large
cash inflows which are hard to generate internally over a short period of time. Whether
or not affiliated firms use the funds raised in an IPO to feed internal capital markets
remains an open question, which we seek to answer in this paper.
See Claessens et al. [2002], Faccio and Lang [2002]
Previous research suggests that an additional rationale for business groups is enhanced control
(e.g., Almeida and Wolfenzon [2006]).
In the similar setting of multi-division firms, the efficiency of internal capital markets meets both
supporting (Giroud and Mueller [2015]) and conflicting (Shin and Stulz [1998]) evidence.
Recent literature has focused on the relationship between internal capital markets,
control rights, and group IPOs. Consistently with the view that group firms have more
availability of internal capital for growth and higher costs associated with loss of control,
Larrain et al. [2021] show that group firms are more selective (i.e., larger and older) and
engage less in market timing when going public than standalone firms. Our detailed
data on ownership structures for the universe of firms (not only for those that eventually
go public) allows us to revisit these results. Specifically, we show that despite the fact
that group IPOs are larger in terms of money raised, a lower share of IPO capital is
invested in productive assets of the newly listed firm, and that assets and employment
of other non-listed affiliated firms grow after the IPO. This suggests that, rather than
pursuing similar goals as stand-alone firms while being more “selective” in their listing
decisions, group firms go public to support other affiliated firms in the group. The
importance of control is emphasized by Masulis et al. [2020] who show that controlling
families of listed groups prefer to fund novel projects by creating new separate public
firms rather than issuing seasoned equity that critically dilutes family control rights
in the issuing firm. Specifically, they show that group internal capital accumulation
positively predicts the likelihood of an IPO but not the likelihood of an SEO. This
suggests that internal capital markets can be employed to incubate new projects, and
that, when investment needs outgrow internal funding capacity, groups restort to IPOs
in order not to dilute ownership in the parent company. Thus, while Masulis et al.
[2020] emphasize the role of internal capital markets in the lead-up to affiliated IPOs,
we examine what happens after the listing event, and in particular we ask whether
newly listed firms “give back” to the group by (partly) sharing the resources raised
from public markets.
Data Collection and Descriptive Statistics
This study relies on four datasets. First, we use the income statement and balance
sheet information of the universe of the Italian limited-liability firms provided by the
National Official Business Register and collected by Cerved Group (a private consulting
firm). Our sample includes all private non-financial companies from 2005 to 2019 with
total assets worth at least 1 million euro, strictly positive revenues and non-negative
equity. Second, we use data from the Infocamere database, which is based on information collected by the Italian Chambers of Commerce. It contains yearly data on firms’
ownership structure, including the type of shareholders (corporate vs individuals) and
the equity share owned by majority shareholders. The third data source consists of
social security payments made by legal entities to the Italian National Social Security
Institute (INPS) for all employees with permanent, fixed-term or apprenticeship contracts. INPS collects information for all private sector firms operating in Italy, with
at least one employee during each calendar year. We use this dataset to retrieve data
at firm level on the average number of employees over the year, share of work force
by occupational categories (blue collars, white collars, managers, apprentices, others),
the monthly average gross wage bill by worker category, and the total number of employees in each month and year. Finally, we use data provided by the Italian Stock
Exchange (Borsa Italiana) to identify companies that became publicly listed between
2006 and 2020. From our analysis of IPOs we exclude listings of investment vehicles
and financial, real estate, blank-cheque and foreign companies. We also exclude companies that go public again after having previously delisted. As a result, our IPO sample
includes 224 newly-listed firms, for which we collect data on the IPO date, the number
of primary and secondary shares issued, the IPO price and proceeds, and the sponsor
or nominated advisor (Nomad). We also gather information on the listing exchange of
choice, distinguishing between the Mercato Telematico delle Azioni (MTA), which is
the main trading platform for listed shares, and the second-tier segment reserved for
small and medium enterprises (SMEs), which we refer to as the Alternative Investment
Market (AIM).8 The requirements to obtain admission on the AIM are less stringent
than those for the MTA, for example there is no lower limit on market capitalization
(40 mil. euros on the MTA) and the minimum free float is 10% (25% on the MTA).
Importantly, on the AIM there are no mandatory corporate governance rules over and
above those established by law for private firms, while MTA listed firms must either
comply with the standard governance code recommended by the regulator or provide an
alternative governance code, explaining the reasons for deviating from the regulator’s
recommendation.
We define a firm as corporate owned if its largest shareholder is another company the “immediate owner”- rather than an individual, provided that the immediate owner’s
share is at least 20%. By recursively identifying immediate owners for all firms in the
dataset, we link each corporate owned company to its ultimate owner, that is the
company in the chain of control that has no known immediate corporate owner. The
relationship between corporate owned firms and ultimate owners can be direct, if immediate and ultimate owner coincide, or indirect, i.e. featuring one or more intermediate
owners. For example, in Figure 1a all affiliated firms ( firms A, B, and C) are directly connected to the ultimate owner, while in Figure 1b firms B and C are directly
connected to firm A which is directly owned by the ultimate owner. In the first case
the ownership structure has one layer, while in the second case the structure has two
layers. More in general we refer to the number of layers in an ownership structure as
the maximum number of intermediate steps between the bottom and the top of the
stayed substantially constant. Finaldi Russo et al. [2020] note that, in Italy, the increase in the number
of listed firms of the recent two decades has been driven by SMEs’ listings, and the smaller size of
Italian public firms largely explains the differences with Germany and Spain in terms of equity market
capitalization.
ownership pyramid. We define business groups (or simply groups) as the set of firms
with the same ultimate owner at a given point in time. We exclude from this definition
structures where the ultimate owner is a financial institution (e.g. bank trusts) and
single-layer groups where the ultimate owner is a holding company, as these type of
ownership structures are generally set up purely for tax optimization purposes. Notice
that our definition of group is not static, as our data allow us to identify ownership
relationships each year. Thus, groups can change in size and composition over time and
the only time-invariant characteristic is the identity of the ultimate owner. We refer to
all firms in a group, except for the ultimate owner, as affiliated firms, while firms that do
not belong to a group are referred to as stand-alone. One major limitation of our data
is that foreign companies, though identified through a specific flag in the Infocamere
shareholders records, are not included in the CERVED dataset, which implies that no
additional information is available to us for these companies. As a consequence, we
are able to reconstruct the chain of control for each company up the the first foreign
owner (if any), implying that firms that we classify as ultimate foreign owners may in
turn be owned by other domestic or foreign firms. All firm and group level variables
are described in Table 1 .
Table 2 shows descriptive statistics at group level for the approximately 190,000
group-year observations in our dataset. On average, groups are fairly small, comprising
1.9 affiliated firms, and have a flat structure, with 1.2 layers. Groups are also quite
concentrated as 87% of total group sales on average originate from one single company.
We distinguish between foreign and domestic ultimate owners and, within the latter
group, we classify ultimate owners in two types, holding vs industrial. Specifically,
holding parent companies differ from the industrial ones in that their main line of business is to manage and control ownership in operating firms within the group and not to
produce goods or services. Ultimate owners in our sample are predominantly domestic
industrial companies (55%), followed by foreign companies (26%), and domestic holding companies (19%). The average (median) size of domestic ultimate owners’ assets is
120.9 million (11.1 million).
Table 3 compares balance sheet data for affiliated firms (13% of the total firm-year
observations) with those of stand-alone firms. Affiliated firms are larger in terms of
assets (42 versus 9 million on average), marginally younger (18 versus 19 years of age),
and have a larger share of intangible assets over total fixed assets (6% versus 3% on
average), but there are no clear differences in terms of profitability or leverage, nor do
they appear to operate in systematically different sectors (see Figure 2a ). Differences
in financial statements and sectors are more pronounced, as one would expect, when we
compare IPO and non IPO firms. IPO firms are significantly more likely to operate in
manufacturing and IT&Telecom, and less likely to operate in commerce and real estate
(see Figure 2b ). Table 4 shows balance sheet data of non listed firms split in non-IPO
firms and IPO firms in the year before going public. IPO firms are on average much
larger in terms of total assets (over 10 times on average), moderatly younger (2 years),
significantly more profitable (14% vs 7% on average), have larger share of intangibles
(17% vs 4% on average) and, surprisingly, lower leverage (70% vs 74% on average).
Interestingly, affiliated firms are over-represented in the IPO sample. In particular,
while affiliated firms only represent 11% of the total sample, this share increases to
over 50% in the IPO sample, although this ratio varies over time (Figure 3 ). Table
5 suggests that this difference is not simply explained by listing requirements (e.g.
in terms of capitalization) since 66% of affiliated-IPOs, i.e., IPOs where the issuing
firms is part of a business group, are listed on the AIM, i.e. the exchange originally
designed for emerging businesses. Moreover, while affiliated-IPOs are larger, the median
ratio of proceeds over total assets is smaller than for stand-alone firms (36% vs 41%).
Additionally, newly issued (“primary”) shares represent 86% of shares sold in stand-
alone firms IPOs and 66% of those sold in affiliated IPOs on average, implying that
the actual capital increase is 36% of assets for affiliated firms and 47% for stand-alone
firms.
We identify 304 firms belonging to the same group as affiliated-IPO firms in the
sample. These firms (henceforth “group firms”) are exposed to potential IPO spillover
effects and are the main object of this study. Figure 4 shows the industry breakdown.
Similarly to the IPO firms (see Figure 2b ), group firms are less likely to operate
in commerce and real estate and more likely to operate in IT&Telecom as compared
to non-listed firms. However, group firms are twice as likely to operate in services
as compared to both IPO and private firms in the sample, suggesting that some of
these firms may perform a support role in the group (e.g., engineering or management
consulting). Table 6 shows balance sheet and employment data of group firms in the 5
years before and after the IPO. Notably, the mean (median) asset size increases from 266
to 317 millions (from 9 to 11 millions), while both average leverage and turnover ratios
drop from 73% to 70% and from 1.03 to 0.98 respectively. The mean (median) total
employment increases from 341 to 363 employees (from 32 to 49 employees), with no
significant change in the relative shares of worker’s categories (managers, white collars
and blue collars). Most group firms are located in the same region (56%) and operate
in the same sector (53%) as the IPO firm in the group (untabulated).
The Effects of Affiliated IPOs on Group Firms
We examine the effects of affiliated IPOs on group firms by estimating the following
model
Yi,g,T,y = ?P ost IP Oi,y + ?P ost IP Oi,y × Sizeg +
T =?4,T Ó=?1
?T DT + ?i + ?y + ?i,g,T,y
where the subscripts i, g, and y indicate the firm, the group, and the calendar year
respectively, and T represents years relative to IPO, i.e., it is the difference between y
and the group IPO year. We examine outcomes measured in terms of (log of) assets,
F ixed Assets
F inancial Debt
Interests
leverage ( F inancial
), cost of debt ( F inancial
), tangibility ( T angible
Debt+Equity
T otal Assets
et Income
), and (log of) total employment.
profitability ( TNotal
Assets
The sample consists of both treated and control firms. Treated firms are those
belonging to a group where one of the affiliated firms goes public during the observation
period. We restrict the treated sample to firm-year observations starting 4 years before
and up to 4 years after the group IPO (i.e., ?4 ? T ? 4). The control sample is built
by matching each treated firm with the 5 closest firms by asset size which at T = ?1
operated in the same sector and belonged to a non-listed group. Thus, for control firms,
T represents years relative to group IPO of the firm they are matched to. We include
firm-year observations in the control sample starting 3 years and up to 5 years after the
matching year (i.e., ?4 ? T ? 4).
For treated firms the variable P ost IP O equals 1 if T ? 0 and zero otherwise, while
for control firms it is always equal to zero. The variable Sizeg is equal to the number
of firms belonging to the same group as firm i in the year prior to the IPO. We use the
interaction term P ost × Size to account for the fact that any possible effect of affiliated
IPOs on a specific group firm may depend on the size of the group, and in particular
it may be weaker when the group is large, as resources may be spread out across a
larger number of entities. The terms ?i and ?y indicate firm and year fixed effects. To
account for trends in the data we include a set of dummy variables DT for each value
of T between -4 and 4. Therefore, the coefficient ? quantifies incremental effects on
outcome dynamics following a group IPO.
Table 7 shows the estimation results of equation 1. The coefficient estimates in
columns 1 and 2 imply that, following the IPO, group firms experience an increase in
assets (+11%) and a decrease in leverage (-6%), suggesting that the expansion in the
asset base is mostly supported by an increase in equity capital. These effects are not
due to pre-exsisting trends, as the estimated coefficients for the dynamic effects show
in Figures 5a and 5b . The coefficient estimates for the interaction term P ostg,y × Sizeg
(?) imply that, as expected, these effects are smaller for larger groups.
Columns 3 to 5 show that, despite the drop in leverage, affiliated IPOs do not
seem to affect the firm’s cost of debt, asset tangibility, and profitability all indicators
that are generally cross-sectionally correlated with leverage. This suggests that the
recapitalization that follows affiliated IPOs in group firms may not be motivated by
savings in interest costs, nor by the need to invest in intangible assets, which are
possibly more efficiently financed with equity due to the non-pleadgeable nature of
collateral. Similarly, the drop in leverage is not explained by a sudden improvement in
the firm’s ability to generate cash internally (e.g., via larger sales turnover or operating
margins). Rather, column 5 suggests that a reduction in financial leverage is coupled
with an increase in operating leverage, through an expansion of the labor force (+18%).
This is consistent with the view that operating leverage, created by labor claims, and
financial leverage act as substitutes (see Simintzi et al. [2015]). The dynamic effects of
affiliated IPOs on group firms employment are illustrated in Figure 5c.
We explore the effects on employment further in Table 8, which shows coefficient
estimates of the following model
Yi,g,T,y =?1 P ost IP Oi,T × HighLevi + ?2 P ost IP Oi,T × Oldi
+ ?3 P ost IP Oi,T × Largei + ?4P ost IP Oi,T P ostg,y × SameIndustryi
+ ?P ost IP Oi,T + ?P ost IP Oi,T × Sizeg
?T DT + ?i + ?y + ?i,g,T,y
T =?4
where HighLevi = 1 if firm i has leverage above the median of its group at t = ?1 (and
zero otherwise), Oldi = 1 if firm i is older than the median of its group at t = ?1 (and
zero otherwise), Largei = 1 if firm i has assets size above the median of its group at
t = ?1 (and zero otherwise), SameIndustryi = 1 if firm i operates in the same industry
as the affiliated firm in its group that goes public at t = 0. The sample includes both
treated and control firms as in equation (1)
The results show that the effects on total employment (column 1) are stronger for
more levered, younger, and smaller firms, the most financially constrained units in the
group. In terms of workforce composition (columns 2 to 4), the share of managers
tends to increase more in large, younger, less constrained firms, and in particular in
firms operating in the same sector as the affiliated IPO firm in the group. In this last
case only, the share of blue collar workers decreases and, consequently, the firm-level
average wage increases significantly.
To summarize, affiliated IPOs seem to unlock fresh capital contributions for group
firms, which are employed for new investments in human capital. What is the exact
origin of these new resources and how do they get transferred to group firms? There
are at least two channels through which new capital can be funneled from public into
internal capital markets following an affiliated IPO. The first is a direct wealth channel:
ultimate owners receive cash inflows from the sale of secondary shares in the IPO firm
(or from the sale of exisitng shares in the markets after the IPO), that can be redeployed
in investments in other group firms. The second is an indirect liquidity channel: as the
ultimate owner’s portfolio becomes more liquid following the listing event, group firms
can reduce their payout ratios and retain a larger share of profit. In our dataset we
observe level of equity capital, but we cannot distinguish between contributed capital
and retained earnings. As such, we cannot precisely attribute changes in assets to the
direct or the indirect channels mentioned above. However, we show that the increase in
assets documented in Table 7 is more pronounced when the affiliated IPO features the
sale of secondary shares. Specifically, we augment the regression in equation (1) with
the interaction term P ost IP Oi,T × Secondaryg, where secondary is a dummy variable
that takes value 1 if secondary shares were sold in the affiliated IPO. The results in
Table 9 column 1 suggest that affiliated IPOs with secondary shares sales are associated
with larger increase in group firms assets. We obtain similar results when we examine
the effects on (log of) equity capital (Table 9 column 2). Moreover, in column 3 we show
that the ownership share of the largest shareholder increases significantly after the IPO,
which is consistent with additional capital contributions of existing shareholders (while
earning retention does not affect ownership concentration). Nevertheless, the estimated
coefficients in Table 9 columns 1 and 2 show that part of effects on assets and equity
capital post IPO are not explained by the sale of secondary shares, suggesting also a
possible role for the liquidity channel.
Group Affiliation and the Going-public Decision
The results presented so far suggest that affiliated IPOs relax financial constraints of
group firms. Is this a simple side effect or an intended objective of affiliated IPOs? To
answer this question we revisit existing evidence on the determinants of IPOs, accounting for group affiliation.
Perhaps the most intuitive reason – though certainly not the only one – for going
public is to raise capital for new investments. Since debt capital is the most common
form of external finance for small and private firms (Berger and Udell 1995; Berger and
Udell 2002; Robb and Robinson 2014), companies are more likely to raise equity capital
on public markets once they exhaust their borrowing capacity, i.e., when debt-to-equity
ratio is relatively high. Indeed, prior literature on IPOs has documented the positive
correlation between firm leverage and the decision to issue new shares (e.g., Kim and
Weisbach [2008]). Another related robust empirical pattern is the relationship between
industry-specific stock market valuations (as measured by market-to-book ratios) and
IPOs (see Pagano et al. [1998]), which suggests that firms go public when there are good
investment opportunities in their sector. Taken altogether, these findings are consistent
with the view that firms list their shares when they face large investment needs which
cannot be fully sourced through other channels (e.g. using internal/private equity or
bank loans).9
This explanation, however, seems to apply better to stand-alone rather than affiliated IPOs. Affiliated firms can access internal capital markets, which alleviates
the financing constraints that stand-alone firms face when sourcing funds on external
capital markets. Ultimate owners can reshuffle resources within the group to finance
profitable investment opportunities, allowing affiliated firms with the best projects to
pursue growth more aggressively than similar stand-alone firms (see Bena and OrtizMolina [2013]). This implies that leverage should be a less important determinant of
IPOs since, differently from stand-alone firms, affiliated firms can tap into the group’s
resources. This also implies, however, that, once the target scale is achieved and external capital markets become accessible on more favorable terms, mature affiliated firms
may be required to “give back” to the group by redirecting externally sourced capital
towards other firms in the group.10 Thus, the IPO of an affiliated firm may be motivated
by the investment needs of the group firms (thus generating the outcomes we document
in the previous section) rather than those of the issuer itself. To the extent that group
firms operate in different sectors, affiliated firms IPOs may correlate with valuations at
the broader market level rather than industry-specific market-to-book ratios.
In Table 10 we test these predictions by examining the listing decisions of affiliated
The empirical findings mentioned here have potential alternative explanations. Firms may go public to restructure their balance sheet (see Pagano et al. [1998]) or to exploit a “window of opportunity”
offered by temporary overvaluations in the stock market, rather than to raise capital for investments.
By examining the use of IPO funds (as in Kim and Weisbach [2008]), we show that these motivations
find less empirical support in our sample.
See Dai et al. [2020] for a theoretical role of spin-offs in internal capital markets.
and stand-alone companies both combined (columns 1 and 2) and separately (columns
2 to 6). In Table 10a we use the following logit model
P r (IP Oi,j,t+1 ) = f ?Leveragei,j,t + ? (M tB)j,t + ?Xi,j,t + ?j + ?i,j,t
where IP Oi,j,t+1 = 1 if firm i goes public in year t + 1, and j indicates broad industry
categories (IT&Telecom, Manufacturing, Other). In Table 10b we show results for the
estimation of the analogous linear probability model where IP Oi,j,t+1 = 100 if firm i
goes public in year t + 1, and zero otherwise. We focus on two explanatory variables,
Leveragei,j,t, i.e., the ratio of debt to total assets of firm i in year t, and M tBj,t, i.e.,
industry j’s average Market-to-Book Enterprise Value in year t. We also estimate an
alternative specification where we replace M tBj,t with M tBt, i.e., the all-industries
average Market-to-Book Enterprise Value in year t. Controls in Xi,j,t include age, sales
growth, ROA quintile, share of intangible assets, and ownership concentration as defined
in Table 1.
Using the full sample of firm-year observations, we show that, as in previous literature, leverage correlates positively and significantly with subsequent IPO events (Table
10, columnn 1 and 2). However, when we split the sample in stand-alone (Table 10,
columns 3 and 4) and affiliated (Table 10, columns 5 and 6) firms, leverage appears
to be significantly correlated with IPO only for the first sub-sample, suggesting that
affiliated firms that go public are not significantly more financially constrained than
those that stay private.
Second, the decision to list shares on a stock exchange positively depends on public
equity valuations, and in particular on market-to-book ratios, as established by prior
studies. This is consistent with the interpretation that firms listing decisions respond
either to future investment opportunities, as measured by market-to-book ratios, or
to market timing considerations, as firms can sell shares at higher prices when market
sentiment is high. However, while the listing of stand-alone firms correlates only with
industry-specific ratios (see column 3 vs column 4), that of affiliated firms correlates
with market-wide ratios (see column 5 vs column 6). Said differently, the going-public
decision of affiliated firms is affected by factors that are not firm-specific. Both the
investment opportunities and the market-timing explanations may apply here. By
going public, affiliated firms raise fresh capital to support investments in other group
firms that operate in different sectors, as our analysis in the previous section suggests.
Alternatively, as shown by Faccio et al. [2021], stock prices of affiliated firms incorporate
the expectation that any firm-specific shock can be absorbed by intra-group cash flows
transfers and therefore tend to have less idiosyncratic returns and track the broad
market more closely. Importantly, both explanations build on the assumption of listed
firms’ active participation in the group’s internal capital market.
It is worth noticing that factors such as sales growth, profitability, firm age, asset
size, and share of intangible assets are significantly correlated (and with the expected
sign) with the probability of IPO in the following period. Moreover, the coefficients
for all controls (except Ownership Concentration) are fairly similar in magnitude and
statistical significance across the affiliated and stand-alone samples, suggesting that
stand-alone and affiliated firms do not differ substantially in how those factors affect
their listing decisions.
The results illustrated above are consistent with the view that affiliated firms IPOs
are less likely to respond to the issuer’s investment needs and financing constraints,
which is also in line with the larger portion of secondary shares sold on average in
the IPO by affiliated firms (34% vs 14%). If affiliated firms raise public equity at
least in part to support other firms in the group, as the internal markets argument
suggests, we should observe that the capital raised with the IPO is less likely to be
invested in the issuer’s own productive assets. In other words, we should observe
significant differences in the use of proceeds between affiliated and stand-alone firms,
and in particular, we should expect a larger effect on liquid assets and a smaller increase
in working capital and fixed assets. Additionally, firms with large investment needs may
couple the issuance of new equity with new debt financing. We expect this effect to be
less significant for affiliated firms.
We examine the effects of IPOs on firm’s assets composition and capital structure
by estimating the following model
Yi,t =?P roceedsi,t + ?P roceedsi,t × Af f iliatedi
+ ?Dt + ? (Dt × Af f iliatedi ) + N etIncomei,t + ?i + ?i,t
where the outcome variable is the amount of firm i’s total assets, liquid assets (i.e.,
cash and cash equivalents), working capital (i.e., accounts receivable and inventory),
fixed assets, equity capital or debt capital. For this analysis we use all IPO firms plus
a control sample of matched firms.11 The variable P roceeds takes the value 0 in the
years leading to the IPO (and every year for matched firms) and the value of total
proceeds from the sale of primary shares in the IPO year and afterwards. Therefore,
the coefficient ? measures the effects of the IPO proceeds on the issuer’s balance sheet
figures (assets, equity, and debt). The variable Af f iliatedi equals 1 if firm i belongs
to a business group in the year prior to the IPO. The coefficient of the interaction
term P roceedsi × Af f iliatedi captures differences in these effects between affiliated
and stand-alone firms. The variable Dt is meant to capture possible linear trends in
the data and it is computed as the number of years before or after the IPO, with
Dt = 0 being the IPO year. For each matched firm, the value of Dt is the same as for
the IPO firm they are matched to. We restrict observations in this analysis to be in
These control firms are selected by means of an algorithm that matches each firm i that went
public in year t + 1 with up to ten private firms in the whole dataset that in year t were closest in
assets size to firm i (within a tolerance band of +/- 20% of firm i’s assets), operated in the same sector
and had the same affiliation status as firm i.
the range ?5 ? t ? 5, in order to focus on the years surrounding the financing event.
We also control for the interaction term Dt × Af f iliatedi to allow for the possibility
of differences in trends between affiliated and stand-alone firms. Finally, N etIncomei,t
accounts for internally generated funds. All specifications include firm fixed effects.
The results in Table 11 show that, for stand-alone firms, assets increase with IPO
proceeds by a factor of approximately 3 (column 1), implying that each dollar raised in
an IPO translates into a 3 dollars increase in assets, which in turns reflects an increase
of approximately 1 dollar in equity capital and 2 dollars in debt capital (columns 2
and 3). This suggests that IPO proceeds are not used to pay back debt. Rather, new
debt is raised along with fresh equity capital to meet the issuer’s investment needs (
as in Kim and Weisbach [2008]). This is consistent with the results in columns 4 to
6. Liquid assets do not significantly change after the IPO, while new investments in
working capital and fixed capital absorb the entire increase in assets. Taken all together,
this evidence suggests that the primary objective of an IPO for stand-alone firms is to
undertake new investments.12
Importantly, the estimated coefficients for the interaction term P roceedsi,t ×Af f iliatedi
suggest that, as compared to stand-alone firms, affiliated firms are less in need for investment capital and are more likely to keep the IPO proceeds in cash. In particular,
assets expand less (column 1), owing to a lower increase in the level of debt (column 3)
following the IPO. Moreover, in contrast with the evidence for stand-alone firms, the
increase in liquid assets is positive and significant, and accounts for approximately 26%
of the overall asset increase (column 4).
To summarize, our evidence is consistent with the view that stand-alone and affiliated firms have different motivations for going public. While stand-alone firms issue
new equity to finance expansion, affiliated IPOs are partly meant to generate cash flows
Notice that this consistent with issuers also rebalancing their capital structure. Indeed, even if the
level of debt increases, the average leverage ratio drops from 71% to 59% on average after the IPO.
for the ultimate owners (e.g., through the sale of secondary shares) and to retain liquid
assets in the issuer’s balance sheet. These results point to the idea that affiliated firms
issue equity in IPOs to the benefit of other firms in the group.
Stock Market Returns
The functioning of internal capital markets bears important implications in terms of
the returns on equity required by outside investors (i.e., minority shareholders). On the
one hand, the presence of an internal capital market partly insures shareholders against
temporary cash-flow shortfalls, implying higher valuations for affiliated firms stocks. On
the other hand, investors may require a larger premium for affiliated stocks to account
for the possibility of being expropriated through intra-group dealings. Importantly,
this premium should be larger when corporate governance rules are more permissive
(e.g., less protective of minority shareholders interests). In our context, this may occur
when firms are listed on the Alternative Investment Market (AIM). Differently from the
MTA – the main trading platform on the Milan Stock Exchange – the AIM does not
require listed firms to abide by the corporate governance protocol set by the regulator.
This rule is intended to facilitate the access to public markets for small and medium
firms by reducing the organizational costs associated with public listings, but it may also
increase the risk of misappropriation of corporate funds. To validate this conjecture, we
examine monthly stock returns of affiliated and stand-alone IPO firms in our sample.
Specifically, we regress adjusted returns (i.e., stock returns minus the return on the
domestic equity index) on the variable Af f iliatedi as follows
ri,t = ?Af f iliatedi + ?Af f iliatedi × AIMi + ?AIMi + ?t + ?i,t
where AIMi is a dummy variable that takes value 1 if the stock is listed on the Alternative Investment Market, and ?t indicates month-year fixed effects. The estimation
results are presented in Table 12, where we also show the results for two alternative
specifications. In column 2 we augment controls by adding industry and IPO year fixed
effects, while in column 3 we additionally include a dummy variable (Large) that takes
value 1 if the firm is classified as large by the stock exchange, the return on the first
trading day, and the percentage of free floating shares over total shares. Our estimation
results are consistent across all three specifications. Affiliated firms perform approximately 90 basis points better than stand-alone firms on a monthly basis if listed on the
main exchange (MTA), but 70 basis points worse if listed on the AIM. Differences in
firm size, initial underpricing, and timing of the IPO do not drive these results. We
interpret this evidence by suggesting that investors benefit from firm’s group affiliation
provided that corporate governance rules are sufficiently rigorous and transparent so to
protect them from expropriation.
Robustness
To verify that our results do not depend on the specific control sample used in Section
4 we perform our many analysis using an alternative matching algorithm. Specifically,
we use the model in equation 3 to estimate the probability of an IPO for all affiliated
firms. For each never-listed affiliated firm, we identify its maximum propensity score as
the highest estimated IPO probability in the firm-specific time series. We then select
firm-year observations where the maximum propensity score is larger than the median
of the propensity score distribution of Affiliated-IPO firms in the year prior to the IPO.
These observations identify potential affiliated IPO firms, i.e., affiliated firms that at
time t display similar characteristics as actual affiliated IPO firms. Finally, firms that,
at time t, belonged to the same group as the “potential” IPO firm are included in the
control group. We further impose a common support restriction on group size at the
time of the “potential” IPO to match the group size of treated firms. We estimate
equation 1 using this alternative control group. The results in Table 13a show that
assets and employment increase and leverage decreases after the group IPO for treated
firms, with magnitudes similar or larger than in our base case.
One of the main drawbacks of our dataset is that we cannot build the entire chain
of ownership for firms with a non-resident corporate owner. This can affect our estimates by introducing measurement errors. For example, two affiliated firms may be
incorrectly classified as belonging to two different groups when in fact they have the
same (unobservable) ultimate owner. To verify that our main results do not depend
on miss-classifications, we exclude both treated and control firms with foreign ultimate
owners from the estimation sample. The coefficient estimates reported in Table 13b
show qualitatively similar results as in our main analysis.
Finally, Table 13c shows that our estimates are significant even when we cluster
standard errors at the group level, that is when we take into account possible (and
plausible) correlation among firms belonging to the same group.
Conclusions
In this paper we show that affiliated IPOs, i.e., IPOs of firms that belong to a business
group, have significant effects on other group members. In particular, immediately
following the IPO, group firms decrease their leverage by 6% and increase their labor
force by 18% on average. These effects are persistent over the following 3-4 years.
We additionally show that the effects on employment are more pronounced for the
younger, more levered, smaller firms in the group, and that, as compared to stand-
alone, affiliated IPOs seem to be less motivated by the issuer’s investment needs. This
evidence suggests that relaxing financial constraints for group firms is an important
driver for the going-public decision of affiliated companies.
Let us conclude with two remarks. First, our findings should be placed in the
wider context of the changing regulation worldwide that aims at facilitating access to
public capital markets, especially for small and young firms. Our study suggests that,
while affiliated IPOs are not necessarily in contrast with the objective of these policies,
corporate governance requirements associated with public listings, which are normally
less strict on new “entrepreneurial” markets, should account for group affiliation, and
in particular for the possibility that the funds raised in an IPO may be diverted into
the internal capital markets, as our results suggest.
Second, our insight may extend beyond business groups, as strategic alliances among
firms – similar to those generated by common ownership – may be established through
different links. For example many industries are characterized by strong and sometime exclusive supplier-customer or creditor-borrower relationships, and it is possible
that seemingly independent financing decisions (such as an IPO) originate within these
informal networks of firms. We leave this question to future research.
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Table 1: Variables Description
ables
Level
Definition
Data Source
Firm’s total assets, measured in millions of euros
Years since firm’s registration
Earnings before net interest payments, taxes, depreciation and amortization
Earnings before net interest payments and taxes
Net Income /Assets
Quintiles of ROA (unlisted firms)
Intangible Fixed Assets/Assets
EBITDA/Assets
Total Debt/Assets
Salest /Salest?1 ? 1
CERVED
CERVED, INPS
CERVED
Largest Ownership Share
Infocamere
Average Number of Employees
Managers/Employment
White Collar Workers/Employment
Blue Collar Workers/Employment
Number of affiliated firms in a group
Maximum number of intermediate owners between any affiliated firm
and the ultimate owner
Weighted average of affiliated leverage. The weights are given by the
relative share of total group sales
The maximum share of total group sales
Non-domestic ultimate owner (dummy)
Ultimate owner is holding company (dummy)
Ultimate owner is an industrial company (dummy)
CERVED, Infocamere
CERVED, Infocamere
Vari-
Assets
EBITDA
ROA Class
Share Intangibles
Profitability
Leverage
Sales Growth
Ownership Concentration
Employment
Share Managers
Share White Collar
Share Blue Collar
CERVED
CERVED
CERVED
CERVED
CERVED
CERVED
CERVED
Group Level Variables
Group Size
Group Layers
Group Leverage
Concentration
Foreign UO
UO Type: Holding
UO Type: Industrial
CERVED, Infocamere
CERVED,
CERVED,
CERVED,
CERVED,
Infocamere
Infocamere
Infocamere
Infocamere
Figure 1: Business Groups: Stylized Examples
These charts represent stylized examples of a single-layer (panel a) and multi-layer (panel b) group organization.
Table 2: Business Groups
This table shows descriptive statistics for all group-year observations in the sample. All variables are defined in Table 1.
Group Size
Group Layers
Concentration
Foreign UO
UO Type: Industrial
UO Type: Holding
UO: Assets (Eur Mil.)
120.91
11.13
2,004.78
count
192,120
192,120
192,120
192,120
192,120
192,120
142,135
Table 3: Financial Statements: Affiliated vs Stand-Alone
This table shows descriptive statistics for all firm-year observations in the sample, split between stand-alone and affiliated
firms. All variables are defined in Table 1.
Assets (Eur Mil.)
Profitability
Turnover
Share Intangibles
Leverage
Observations
Stand-alone
18.86
16.00
348.47
17.25
Affiliated
41.67
17.86
351639
14.00
502.53
27.06
Table 4: Financial Statements: Public vs Private
This table shows descriptive statistics for private firms and for IPO firms. The statistics for IPO firms refer to the year
prior to the IPO. All variables are defined in Table 1.
Assets (Eur Mil.)
Profitability
Share Intangibles
Leverage
Observations
No IPO
19.656
19.007
0.074
0.037
0.737
2.638
16.000
0.059
0.004
0.807
666.662
19.162
0.099
0.094
0.233
count
256.684
16.688
0.140
0.170
0.698
26.475
12.000
0.128
0.088
0.738
1131.723
17.617
0.129
0.200
0.181
count
Figure 2: Industries
These graphs show the industry break-down for stand-alone vs affiliated firms (panel a) and non-IPO and IPO firms
(panel b).
Table 5: IPOs: Stand-alone vs Affiliated
This table shows descriptive statistics for the IPOs of stand-alone and affiliated IPOs. AIM is a dummy variable that
takes value 1 if the IPO is on the AIM market segment. Proceeds are the total IPO proceeds in million euros. Assets
refers to firm’s assets the year prior the IPO. Primary shares in the share of primary shares over total shares sold. Capital
increase is equal to Proceeds*Primary Shares.
Proceeds
Proceeds/Assets
Primary Shares
Capital Increase /Assets
Observations
Stand-alone
63.66
232.28
Affiliated
123.21
14.13
348.12
Figure 3: IPOs
This figure plots the total number of IPOs (right axis) and the share of affiliated IPOs (left axis) per year.
Table 6: Group Firms
table
shows
years
balance
before
sheet
after
employment
group
group
(a) Balance Sheet
Assets (Eur Mil.)
Profitability
Turnover
% Fixed Assets
% Intangibles
Leverage
Group Size
Observations
Pre IPO
265.53
14.51
12.00
1,932.92
12.29
Post IPO
316.91
17.22
11.36
14.00
1,929.57
12.53
49.00
1,674.74
(b) Employment
Employment
% Managers
% White Collars
% Blue Collars
Observations
Pre IPO
341.08
32.08
2,009.89
Post IPO
362.52
firms
member.
Figure 4: Group Firms: Industry
These chart shows the industry break-down for group firms, i.e., firms that belong to the same group as affiliated-IPO
firms.
Table 7: Effects of Affiliated IPOs on Group Firms
This table shows coefficient estimates for six linear regressions of (log of) assets, leverage, cost of debt, ROA, tangibility,
and (log of) total employment of firm i. Post IPO is a dummy variable that takes value 1 in the years after the group-IPO
year and zero otherwise. Size is the number of firms belonging to the same group as firm i the year before the groupIPO. DT is a dummy variable for each value of T between -4 and 4, where T represents years relative to group-IPO. All
specifications include firm and year fixed effects. The sample consists of both treated and control firms. Treated firms
are those belonging to a group where one of the affiliated firms goes public during the observation period. The control
sample is built by matching each treated firm with the 5 closest firms by asset size which at T=-1 operated in the same
sector and belonged to a non-listed group. Standard errors in parentheses. *, **, and *** indicate statistical significance
at the 10%, 5%, and 1%, respectively.
Post IPO
Post IPO X Group Size
Firm and Year FE
Observations
Firms
R-Squared
Mean Dep.
Assets
0.1063???
(0.0343)
Leverage
-0.0665???
(0.0207)
Debt Cost
0.0486
(0.1345)
Tangibility
0.0101
(0.0086)
-0.0108
(0.0120)
Employment
0.1831???
(0.0494)
-0.0100???
(0.0037)
0.069
0.0064???
(0.0022)
0.025
-0.0056
(0.0141)
0.005
-0.0005
(0.0009)
0.015
0.0027??
(0.0013)
0.009
-0.0170???
(0.0053)
0.016
Figure 5: Dynamic Effects of Affiliated IPOs on Group Firms
These graphs show coefficient estimates for ?T in the following regression Yi,T,y =
? D XGroupIP O +
T =?4 T T
P ostIP OXSize + ?i + ?y + ?i,y,T . Post IPO is a dummy variable that takes value 1 in the years after the group-IPO
year and zero otherwise. Size is the number of firms belonging to the same group as firm i the year before the groupIPO. DT is a dummy variable for each value of T between -4 and 4, where T represents years relative to group-IPO. All
specifications include firm and year fixed effects. Yi,T,y is equal to (log of) assets, leverage, and (log of) total employment
in panel (a), (b) and (c) respectively. The sample consists of both treated and control firms. Treated firms are those
belonging to a group where one of the affiliated firms goes public during the observation period. The control sample is
built by matching each treated firm with the 5 closest firms by asset size which at T=-1 operated in the same sector
and belonged to a non-listed group.
Table 8: Effects on Employment
This table shows coefficient estimates for five linear regressions of (log of) total employment, share of managers, share of
white collars, share of blue collars, and (log of) average salary of firm i. The sample includes all group firms plus up to
5 affiliated control firms, matched on the basis of industry and asset size in the year prior to the IPO. P ost = 1 if one of
the members in firm i’s group is public (and zero otherwise). HighLevi = 1 if firm i has leverage above the median of its
group at t=-1 (and zero otherwise), Oldi = 1 if firm i is older than the median of its group at t=-1 (and zero otherwise),
Largei = 1 if firm i has assets size above the median of its group at t=-1 (and zero otherwise), SameIndustryi = 1
if firm i operates in the same industry as the affiliated firm in its group that goes public at t=0. Standard errors in
parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.
Employment
0.2778???
(0.0735)
% Managers
0.0055
(0.0050)
% White Collar
0.0053
(0.0141)
% Blue Collar
0.0050
(0.0130)
Avg. Wage
-0.0213
(0.0301)
Post X High Lev.
0.1146??
(0.0473)
-0.0088???
(0.0032)
0.0146
(0.0090)
-0.0067
(0.0083)
-0.0256
(0.0193)
Post X Old
-0.1293??
(0.0506)
-0.0138???
(0.0034)
0.0021
(0.0096)
0.0083
(0.0089)
-0.0155
(0.0206)
Post X Large
-0.0924?
(0.0512)
0.0098???
(0.0034)
-0.0133
(0.0098)
0.0021
(0.0090)
0.0377?
(0.0208)
Post X Same Industry
-0.0265
(0.0468)
0.0158???
(0.0031)
-0.0004
(0.0089)
-0.0153?
(0.0082)
0.0667???
(0.0190)
-0.0179???
(0.0054)
0.019
-0.0013???
(0.0004)
0.015
-0.0006
(0.0010)
0.020
0.0010
(0.0010)
0.021
-0.0012
(0.0022)
0.051
Post X Size
Firm and Year FE
Observations
Firms
R-Squared
Mean Dep.
Table 9: Liquidity Effects on Group Firms Capital Structure
This table shows coefficient estimates for three linear regressions of (log of) assets, (log of) equity, and ownership
concentration of firm i. Post IPO is a dummy variable that takes value 1 in the years after the group-IPO year and zero
otherwise. Secondary is a dummy variable that takes value of one if existing shares were sold in the affiliated IPO. Size
is the number of firms belonging to the same group as firm i the year before the group-IPO. DT is a dummy variable
for each value of T between -4 and 4, where T represents years relative to group-IPO. All specifications include firm and
year fixed effects. The sample consists of both treated and control firms. Treated firms are those belonging to a group
where one of the affiliated firms goes public during the observation period. The control sample is built by matching
each treated firm with the 5 closest firms by asset size which at T=-1 operated in the same sector and belonged to a
non-listed group. Standard errors in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and
1%, respectively.
Post IPO X Secondary
Post IPO
Post IPO X Group Size
Firm and Year FE
Observations
Firms
R-Squared
Mean Dep.
Assets
0.1317???
(0.0343)
Equity
0.1330??
(0.0655)
Ownership Concentration
0.0672?
(0.0372)
0.1205?
(0.0711)
0.0478???
(0.0084)
-0.0161???
(0.0038)
0.055
-0.0193???
(0.0073)
0.069
-0.0034???
(0.0009)
0.027
Table 10: IPO Determinants: Affiliated vs Stand-Alone
This table shows estimates of odds ratios (panel a) or linear coefficients (panel b) for the probability of an IPO at time
t + 1 for firm i on explanatory variables measured at t. The sample includes all private firms (columns 1 and 2), all
private stand-alone firms (column 3 and 4), or all affiliated private firms (columns 5 and 6). Leverage is firm i’s ratio
of total debt over total assets. Mkt-to-Book (Industry Specific) is the average ratio of the U.S. stock market value over
book value for firm i’s sector (IT&Telecom, Manufacturing, or Other). Mkt-to-Book (Industry Specific) is the average
ratio of the U.S. stock market value over book value averaged across industries. All other variables are described in Table
1. Standard errors in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.
(a) Logit
Leverage
Mkt-to-Book (Industry Specific)
All Firms
1.0460???
(0.3533)
All Firms
0.9922???
(0.3521)
Stand-Alone
1.6571???
(0.5374)
0.1475??
(0.0701)
Mkt-to-Book (All Industries)
Stand-Alone
1.6330???
(0.5355)
0.1868??
(0.0822)
Affiliated
0.5925
(0.4670)
0.1088
(0.1166)
0.5088
(0.3286)
0.7582???
(0.2322)
Affiliated
0.5254
(0.4654)
0.9416???
(0.3164)
Sales Growth (1 lag)
0.4663???
(0.0657)
0.4561???
(0.0660)
0.4997???
(0.0949)
0.4973???
(0.0954)
0.3991???
(0.0899)
0.3825???
(0.0901)
ROA Class
0.5534???
(0.0677)
0.5543???
(0.0678)
0.6914???
(0.1073)
0.6902???
(0.1073)
0.4428???
(0.0815)
0.4418???
(0.0816)
Age (yrs)
-0.0025???
(0.0004)
-0.0025???
(0.0004)
-0.0038???
(0.0005)
-0.0038???
(0.0005)
-0.0015???
(0.0004)
-0.0015???
(0.0004)
Share Intangibles
3.1401???
(0.3005)
3.1763???
(0.3005)
3.2619???
(0.4460)
3.2788???
(0.4452)
2.9346???
(0.3717)
2.9927???
(0.3734)
Ln(Assets)
0.7295???
(0.0278)
0.7297???
(0.0277)
0.7746???
(0.0406)
0.7750???
(0.0405)
0.5737???
(0.0418)
0.5727???
(0.0416)
Ownership Concentration
-0.4825??
(0.2392)
-0.4780??
(0.2400)
-0.5813
(0.3539)
-0.5724
(0.3545)
-2.0711???
(0.4002)
-2.0429???
(0.3990)
Industry FE
Observations
Firms
Mean Dep. Var.
Pseudo-R2
2,595,880
352,615
8.59e-05
0.1986
2,595,880
352,615
8.59e-05
0.1999
2,259,421
326,648
4.56e-05
0.2151
2,259,421
326,648
4.56e-05
0.2149
336,459
63,909
3.57e-04
0.1484
336,459
63,909
3.57e-04
0.1516
Stand-Alone
0.0045??
(0.0018)
Stand-Alone
0.0044??
(0.0018)
(b) LPM
Leverage
Mkt-to-Book (Industry Specific)
All Firms
0.0055??
(0.0025)
All Firms
0.0051??
(0.0025)
0.0020??
(0.0008)
0.0024??
(0.0011)
Mkt-to-Book (All Industries)
0.0069???
(0.0022)
Affiliated
0.0134
(0.0134)
Affiliated
0.0118
(0.0134)
0.0052
(0.0060)
0.0024
(0.0016)
0.0379???
(0.0137)
Other Controls
Industry FE
Observations
Firms
Mean Dep. Var.
Adj.-R2
2,595,880
352,615
8.59e-03
0.0007
2,595,880
352,615
8.59e-03
0.0007
2,259,421
326,648
4.56e-03
0.0005
2,259,421
326,648
4.56e-03
0.0005
336,459
63,909
3.57e-02
0.0012
336,459
63,909
3.57e-02
0.0013
Table 11: Use of IPO Proceeds: Affiliated vs Stand-Alone
This table shows coefficient estimates for six linear regressions of assets, equity, total debt, working capital and fixed
assets of firm i. The sample includes IPO firms plus a matched sample of private firms operating in the same sector, with
the same affiliation status, and of similar size as the IPO firms. P roceeds equals zero before the IPO (or at any time
for the matched sample) and the amount of total primary shares IPO proceeds after the IPO. Af f iliated is a dummy
variable that takes value 1 if firm i belongs to a business group. t is the number of years before or after the IPO (with
t=0 being the IPO year). Matched firms are associated with the same t as the IPO firms they are matched to. All
specifications include firm fixed effects. Standard errors in parentheses. *, **, and *** indicate statistical significance
at the 10%, 5%, and 1%, respectively.
Assets
3.0339???
(0.2018)
Equity
1.1920???
(0.1100)
1.8419???
(0.1550)
Liquid Assets
-0.0324
(0.0294)
Working Capital
1.2494???
(0.0924)
Fixed Assets
1.8169???
(0.1528)
Proceeds X Affiliated
-1.4001??
(0.5494)
-0.4179
(0.2995)
-0.9822??
(0.4219)
0.4219???
(0.0801)
-0.4904?
(0.2515)
-1.3316???
(0.4160)
1.1331
(1.0475)
0.8475
(0.5710)
0.2857
(0.8044)
0.5829???
(0.1527)
-0.5840
(0.4795)
1.1342
(0.7931)
t X Affiliated
8.5677???
(1.4199)
5.1537???
(0.7740)
3.4140???
(1.0904)
0.5167??
(0.2069)
2.7758???
(0.6500)
5.2752???
(1.0751)
Net Income
0.0743???
(0.0277)
14764
0.029
213.00
0.3712???
(0.0151)
14764
0.067
74.04
-0.2969???
(0.0213)
14764
0.029
138.96
0.0385???
(0.0040)
14764
0.017
11.82
0.0789???
(0.0127)
14764
0.021
85.95
-0.0431??
(0.0210)
14764
0.019
115.23
Proceeds
Firm FE
Observations
Firms
R-Squared
Mean Dep.
Table 12: Post IPO stock market returns: Affiliated vs Stand-Alone
This table shows coefficient estimates for a linear regression of monthly excess stock returns of firm i. Af f iliated is a
dummy variable that takes value 1 if firm i belongs to a business group. AIM Mkt is a dummy variable that takes value
1 if firm i’s share are listed on the Alternative Investment Market. Large is a dummy variable that takes value 1 if firm
i is classified as a large cap by the stock exchange. % Free Float is the share of equity floated on the exchange at the
IPO. Standard errors in parentheses are clustered at the firm level. *, **, and *** indicate statistical significance at the
10%, 5%, and 1%, respectively.
Affiliated
0.0089???
(0.0030)
0.0090???
(0.0032)
0.0084???
(0.0032)
Affiliated X AIM Mkt
-0.0154???
(0.0038)
-0.0158???
(0.0040)
-0.0182???
(0.0041)
0.0051
(0.0031)
0.0044
(0.0033)
0.0090??
(0.0042)
AIM Mkt
Large
0.0061?
(0.0035)
1st Day Return
-0.0028
(0.0075)
% Free Float
-0.0044
(0.0087)
Month-Year FE
Industry FE
16,485
-0.00106
0.3066
16,485
-0.00106
0.3063
15,265
-0.00069
0.3078
IPO Year FE
Observations
Firms
Mean Dep. Var.
Adj. R2
Table 13: Robustness
This table shows coefficient estimates for three linear regressions of (log of) assets, leverage, and (log of) total employment of firm i. Post IPO is a dummy variable that takes value 1 in the years after the group-IPO year and
zero otherwise. Size is the number of firms belonging to the same group as firm i the year before the group-IPO.
DT is a dummy variable for each value of T between -4 and 4, where T represents years relative to group-IPO. All
specifications include firm and year fixed effects. The sample consists of both treated and control firms. Treated
firms are those belonging to a group where one of the affiliated firms goes public during the observation period.
The control sample is built with propensity score matching in Panel (a). In Panel (b) and (c) the control sample is built by matching each treated firm with the 5 closest firms by asset size which at T=-1 operated in the
same sector and belonged to a non-listed group. In Panel (b) we exclude all firms belonging to groups with nonresident (foreign) ultimate owners. Standard errors in parentheses in Panel (a) and (b). Errors are clustered at
the group level in Panel (c). *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.
(a) Propensity Score Matching
Post IPO
Post IPO X Group Size
Firm and Year FE
Observations
Firms
R-Squared
Mean Dep.
Assets
0.1740???
(0.0487)
Leverage
-0.0561?
(0.0292)
Employment
0.2042???
(0.0672)
-0.0216???
(0.0057)
40303
0.056
0.0052
(0.0034)
32744
0.015
-0.0178??
(0.0083)
36033
0.015
(b) Excluding Foreign Ultimate Owners
Post IPO
Post IPO X Group Size
Firm and Year FE
Observations
Firms
R-Squared
Mean Dep.
Assets
0.1820???
(0.0355)
Leverage
-0.0416?
(0.0216)
Employment
0.2682???
(0.0536)
-0.0156???
(0.0037)
0.066
0.0042?
(0.0022)
0.024
-0.0242???
(0.0056)
0.016
(c) Clustered Standard Errors
Post IPO
Post IPO X Group Size
Firm and Year FE
Observations
Firms
R-Squared
Mean Dep.
Assets
0.1063?
(0.0644)
Leverage
-0.0665??
(0.0304)
Employment
0.1831?
(0.0988)
-0.0100
(0.0064)
0.069
0.0064??
(0.0028)
0.025
-0.0170?
(0.0097)
0.016
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Mechanism, by Massimiliano Renzetti, Fabrizio Dinacci and Ann Börestam (Research Papers)
n. 17
What’s ahead for euro money market benchmarks?, by Daniela Della Gatta (Institutional
Issues) (in Italian)
n. 18
Cyber resilience per la continuità di servizio del sistema finanziario, by Boris Giannetto
and Antonino Fazio (Institutional Issues) (in Italian)
n. 19
Cross-Currency Settlement of Instant Payments in a Cross-Platform Context: a Proof of
Concept, by Massimiliano Renzetti, Andrea Dimartina, Riccardo Mancini, Giovanni Sabelli,
Francesco Di Stasio, Carlo Palmers, Faisal Alhijawi, Erol Kaya, Christophe Piccarelle, Stuart
Butler, Jwallant Vasani, Giancarlo Esposito, Alberto Tiberino and Manfredi Caracausi
(Research Papers)
n. 20
Flash crashes on sovereign bond markets – EU evidence, by Antoine Bouveret, Martin
Haferkorn, Gaetano Marseglia and Onofrio Panzarino (Research Papers)
n. 21 Report on the payment attitudes of consumers in Italy: results from ECB surveys,
by Gabriele Coletti, Alberto Di Iorio, Emanuele Pimpini and Giorgia Rocco (Institutional
Issues)
n. 22
When financial innovation and sustainable finance meet: Sustainability-Linked Bonds,
by Paola Antilici, Gianluca Mosconi and Luigi Russo (Institutional Issues) (in Italian)
n. 23
Business models and pricing strategies in the market for ATM withdrawals, by Guerino
Ardizzi and Massimiliano Cologgi (Research Papers)
n. 24
Press news and social media in credit risk assessment: the experience of Banca d’Italia’s
In?house Credit Assessment System, by Giulio Gariano and Gianluca Viggiano (Research
Papers)
n. 25
The bonfire of banknotes, by Michele Manna (Research Papers)
n. 26
Integrating DLTs with market infrastructures: analysis and proof-of-concept for secure DvP
between TIPS and DLT platforms, by Rosario La Rocca, Riccardo Mancini, Marco Benedetti,
Matteo Caruso, Stefano Cossu, Giuseppe Galano, Simone Mancini, Gabriele Marcelli, Piero
Martella, Matteo Nardelli and Ciro Oliviero (Research Papers)
n. 27
Statistical and forecasting use of electronic payment transactions: collaboration between
Bank of Italy and Istat, by Guerino Ardizzi and Alessandra Righi (Institutional Issues) (in
Italian)
n. 28
TIPS: a zero-downtime platform powered by automation, by Gianluca Caricato, Marco
Capotosto, Silvio Orsini and Pietro Tiberi (Research Papers)
n. 29
TARGET2 analytical tools for regulatory compliance, by Marc Glowka, Alexander Müller,
Livia Polo Friz, Sara Testi, Massimo Valentini and Stefano Vespucci (Institutional Issues)
n. 30
The security of retail payment instruments: evidence from supervisory data, by Massimiliano
Cologgi (Research Papers)
n. 31
Open Banking in the payment system: infrastructural evolution, innovation and security,
supervisory and oversight practices, by Roberto Pellitteri, Ravenio Parrini, Carlo Cafarotti
and Benedetto Andrea De Vendictis (Institutional Issues) (in Italian)
n. 32
Banks’ liquidity transformation rate: determinants and impact on lending, by Raffaele Lenzi,
Stefano Nobili, Filippo Perazzoli and Rosario Romeo (Research Papers)
n. 33
Investor behavior under market stress: evidence from the Italian sovereign bond market, by
Onofrio Panzarino (Research Papers)
n. 34
Siamese neural networks for detecting banknote printing defects, by Katia Boria, Andrea
Luciani, Sabina Marchetti and Marco Viticoli (Research Papers) (in Italian)
n. 35
Quantum safe payment systems, by Elena Bucciol and Pietro Tiberi
n. 36
Investigating the determinants of corporate bond credit spreads in the euro area, by Simone
Letta and Pasquale Mirante
n. 37
Smart Derivative Contracts in DatalogMTL, by Andrea Colombo, Luigi Bellomarini, Stefano
Ceri and Eleonora Laurenza
n. 38
Making it through the (crypto) winter: facts, figures and policy issues, by Guerino Ardizzi,
Marco Bevilacqua, Emanuela Cerrato and Alberto Di Iorio
n. 39
The Emissions Trading System of the European Union (EU ETS), by Mauro Bufano, Fabio
Capasso, Johnny Di Giampaolo and Nicola Pellegrini (in Italian)
n. 40
Banknote migration and the estimation of circulation in euro area countries: the italian
case, by Claudio Doria, Gianluca Maddaloni, Giuseppina Marocchi, Ferdinando Sasso,
Luca Serrai and Simonetta Zappa (in Italian)
n. 41
Assessing credit risk sensitivity to climate and energy shocks, by Stefano Di Virgilio, Ivan
Faiella, Alessandro Mistretta and Simone Narizzano
n. 42
Report on the payment attitudes of consumers in italy: results from the ecb space 2022
survey, by Gabriele Coletti, Alberto Di Iorio, Emanuele Pimpini and Giorgia Rocco
n. 43
A service architecture for an enhanced Cyber Threat Intelligence capability and its value
for the cyber resilience of Financial Market Infrastructures, by Giuseppe Amato, Simone
Ciccarone, Pasquale Digregorio and Giuseppe Natalucci
n. 44 Fine-tuning large language models for financial markets via ontological reasoning,
by Teodoro Baldazzi, Luigi Bellomarini, Stefano Ceri, Andrea Colombo, Andrea Gentili
and Emanuel Sallinger
n. 45
Sustainability at shareholder meetings in France, Germany and Italy, by Tiziana De Stefano,
Giuseppe Buscemi and Marco Fanari (in Italian)
n. 46
Money market rate stabilization systems over the last 20 years: the role of the minimum
reserve requirement, by Patrizia Ceccacci, Barbara Mazzetta, Stefano Nobili, Filippo
Perazzoli and Mattia Persico
n. 47 Technology providers in the payment sector: market and regulatory developments,
by Emanuela Cerrato, Enrica Detto, Daniele Natalizi, Federico Semorile, Fabio Zuffranieri
n. 48
The fundamental role of the repo market and central clearing, by Cristina Di Luigi, Antonio
Perrella and Alessio Ruggieri