The impact of bank loan terms on intangible investment in Europe.
Date: 2021-06-05
Institutions: European Investment Bank
Subjects: European Investment Bank, European Union, credit guarantee, economic survey, interest, investment, investment cost, loan, type of business

ECONOMICS – WORKING PAPERS 2021/05

The impact of bank loan terms  
on intangible investment  

in Europe

The impact of bank loan terms on 

intangible investment in Europe 

June 2021 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The impact of bank loan terms on intangible investment in Europe 

© European Investment Bank, 2021 

EIB Working Paper 2021/05 
June 2021 

Authors 
Matthieu Segol (Paris School of Economics) 
Atanas Kolev (European Investment Bank) 
Laurent Maurin (European Investment Bank) 

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ISSN 2599-736X  DOI 10.2867/974979 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The Impact of Bank Loan Terms on Intangible

Investment in Europe

Matthieu Segol∗

Atanas Kolev†

Laurent Maurin‡

March 2021

Abstract

Using European ﬁrm-level data from a new survey, the EIBIS, we document the
eﬀect of bank loan terms on investment in intangible assets of non-ﬁnancial cor-
porations. We show that quantity rationing is a primary determinant borrowers’
propensity to invest in intangible assets. Provided that ﬁrms are satisﬁed with their
loan size; unfavorable rate, maturity and collateral requirements have no signiﬁ-
cant eﬀects on the probability to invest in intangible assets. These terms however,
do have a negative impact on the probability to invest in multiple intangible assets,
undermining the ability of ﬁrms to beneﬁt from the complementarities of these as-
sets. We document the eﬀect of loan conditions on investment intensity, as well.
The eﬀect of quantity rationing on the amount invested in intagible assets is found
to be limited. Other loan conditions however, like cost, maturity and collateral re-
quirements, have signiﬁcant eﬀect on investment intensity.

JEL Codes: G21, D82, O30, H81, C35

Key Words: Firm investment, Intangible assets, Loan terms, Credit constraint, Sur-
vey data, Instrumental variable approach

∗Paris School of Economics, matthieu.segol@psemail.eu, corresponding author.
†European Investment Bank, a.kolev@eib.org.
‡European Investment Bank, l.maurin@eib.org.
Support received from the European Investment Bank Institute through its STAREBEI programme
is gratefully acknowledged. The views expressed in this document are those of the authors and do not
necessarily reﬂect the position of the EIB or its shareholders.

1

1

Introduction

Intangible assets are essential for innovation and long-term growth. Their accumu-

lation, however, is constrained by several market failures resulting from their speciﬁc

characteristics. In particular, asymmetric information and agency problems between

ﬁrms and lenders can result in inadequate external ﬁnance conditions that can ulti-

mately lead to underinvestment in intangible assets. Bank loans for capital expendi-

ture are typically collateralized by the acquired assets. This practice, however, is not

very common for loans that ﬁnance the acquisition of intangible assets, because their

valuation is diﬃcult and the transferring of ownership is often impractical. As a result,

access to external ﬁnancing for investment in intangible assets is reduced. The problem

is especially accute in bank-based economies, such as most European ones.

Empirical evidence on impediments to investment in intangibles is scarce, given the

diﬃculty to measure the amount invested in these assets and the constraints faced by

ﬁrms in obtaining a loan. We use a new survey provided by the European Investment

Bank (EIB), the EIB Investment Survey (EIBIS), to add to the empirical literature on

constraints to investment in intangible assets. EIBIS data allow breaking down the ef-

fects of bank loan terms on investment in several classes of intangibles assets, providing

the possibility to exploit synergies among them. The EIBIS has important advantages

compared to surveys used in previous studies. First, the EIBIS is representative at the

country-, sector- and size level across all European countries. Second, survey respon-

dents are asked to provide the amount invested in several categories of intangible as-

sets: research and development (R&D), which also includes the acquisition of intellec-

tual property; software, data, IT networks and website activities; training of employees;

organisation and business processes improvements. Third, the EIBIS measures the sat-

isfaction of ﬁrms with their external ﬁnance along four diﬀerent dimensions: amount,

cost, maturity and collateral requirements.

These features allow to extend the analysis beyond the existing literature. Most

studies focus on R&D, partly because of its key role in the innovation process, but also

partly because of the lack of data on other assets. Capitalized R&D expenditure rep-

resents only one of the several intangible assets which are important for innovation

2

(OECD 2013) and there are signiﬁcant complementarities and synergies among them

in the production process (Crass and Peters 2014). Regarding the measurement of ﬁ-

nancial constraints, data allow for the use of a direct indicator and thus avoid the limits

of indirect measures based on investment cash-ﬂow sensitivity, which are prevalent in

the literature. To our knowledge, this is the ﬁrst analysis studying the impact of bank

lending on investment in intangible assets with such breadth of information.

Using EIBIS, we reveal a set of new stylized facts and empirical evidence on the

eﬀects of loan conditions on intangible investment in Europe. Our analysis is based

on an instrumental variable approach to properly identify the causal impact of bank

loan conditions on investment in intangible assets. Savignac (2008) and Mancusi and

Vezzulli (2014) use a similar methodology to tackle the endogeneity of the ﬁnancial

constraint indicator. More speciﬁcally, following Mancusi and Vezzulli (2014), we use

the index of external ﬁnance constraints developed by Whited and Wu (2006) as an

instrument for our indicator of satisfaction with loan conditions. We provide evidence

that this approach is valid for our sample.

We ﬁrst document that the loan amount is the primary determinant of the likeli-

hood to invest in intangibles. Quantity rationing has a signiﬁcantly negative eﬀect on

investment propensity.1 Other possibly unfavorable loan terms, such as unsatisfactory

interest rates or capital requirements, have no signiﬁcant impact as long as ﬁrms are

granted a satisfactory loan amount. This is in line with previous analyses documenting

the critical impact of quantity rationing on intangible investment propensity (Mancusi

and Vezzulli 2014, Popov 2014). These studies focus on investment in a single intan-

gible asset, R&D or on-the-job training. We contribute to this literature by showing

that the negative eﬀect of such rationing is more general as it hinders the propensity to

invest in a wide range of intangible assets.2

In order to account for the complementarity between intangible assets, it is essential

1Quantity rationing here is a reduction in the initially requested loan amount, measured by borrow-

ers’ dissatisfaction with the loan size in our data.

2A closely related literature analyzes the impact of a constrained access to bank credit on innovation,
using frameworks diﬀerent from the cash-ﬂow based model. Benfratello et al. (2008) ﬁnd that local
banking development in Italy over the 1990s increased the probability of process innovation in sectors
more dependent on external ﬁnance. Several subsequent empirical analyses conﬁrmed these results
(Savignac 2008, Amore et al. 2013, Nanda and Nicholas 2014, Bircan and De Haas 2015, Qi and Ongena
2018).

3

to measure the eﬀect of loan terms on the propensity to invest in at least two types of in-

tangible assets simultaneously.3 This type of analysis, generally not possible with most

datasets, can be conducted with EIBIS data. When focusing on investment in multiple

assets, our results show that other loan terms have a signiﬁcant negative eﬀect. This

diﬀerence with our ﬁrst result can be attributed to the fact that ﬁrms investing in diver-

siﬁed intangible assets display healthier balance sheets, which implies lower agency

costs. Sounder ﬁnancials allow these ﬁrms to overcome quantity rationing when ap-

plying for loans. For those borrowers, dissatisfaction with the remaining terms are

likely to represent the main constraint. Our results show that dissatisfaction with cost,

maturity and collateral requirements have a negative eﬀect on ﬁrms’ ability to invest

in a portfolio of intangible assets. This suggests that when lenders avoid the extreme

outcome of quantity rationing, they resort to other constraining loan terms, like high

collateral requirements for example.

We document that quantity rationing has a limited eﬀect on the amount invested

in intangible assets. This ﬁnding is in line with previous studies that found that quan-

tity rationing is likely to have a stronger impact on investment propensity rather than

investment intensity. The phenomenon is usually explained by the high adjustment

costs or limited scalability of investment in intangible assets: once started, the size of

the investment cannot be adjusted without large losses. Thus, quantity rationing is

more likely to have an impact on the decision to invest than on the amount invested.

Another contribution of this study is the measurement of the extent to which dissatis-

faction with the remaining terms – cost, maturity and collateral requirements – aﬀects

intangible investment intensity.

The rest of the paper proceeds as follows. Section 2 reviews the literature on the

speciﬁc nature of intangible assets and on the impact of agency costs on intangible in-

vestment. Section 3 presents the dataset used in the paper, the EIBIS, and provides styl-

ized facts on intangible investment and its ﬁnancing. Section 4 describes the diﬀerent

empirical models used to analyze the eﬀects of loan terms on investment in intangibles.

Section 5 discusses the results and Section 6 concludes.

3Firms investing in several types of intangibles are those investing in several assets for a speciﬁc

project, i.e. a "multi-asset" investment.

4

2 Related literature

Intangible investment represents an investment in capital that is diﬀerent from physical

or ﬁnancial assets.4 Intangibles, also called knowledge assets, are mostly embodied in

people (Andrews and Serres 2012). They share distinctive characteristics, extensively

deﬁned by Andrews and Serres (2012), which make them particularly hard to ﬁnance

using external funds. First, most intangibles are generally non-marketable, i.e. they can-

not be traded on designated markets. Second, they are non-separable, i.e. these assets

cannot be separated from the original unit of creation without some loss of value (Web-

ster and Jensen 2006). Third, they are characterized by a lack of visibility in the sense

that, without physical embodiment, it is diﬃcult to assess the stock of an intangible

asset based on past investment ﬂows. Fourth, given that they correspond to a speciﬁc

knowledge, transferability of intangible assets depends on whether this knowledge is

tacit or codiﬁed. Finally, investment in intangible assets entails exposure to uncertainty,

above and beyond that of investment in tangible assets.5

These speciﬁc characteristics explain why ﬁrms investing in these assets are likely

to face important information asymmetries when seeking external ﬁnance.

Indeed,

lenders have diﬃculty valuing such assets, because of the embodied uncertainty, lack

of visibility and non-tradability. Moreover, ﬁrms have incentives to limit information

about their investment for fear of leak of information to competitors. Another limita-

tion is the incomplete account for intangible assets in ﬁrms’ balance sheets. The reason

is that existing accounting frameworks do not measure properly some of types of in-

tangible assets, due to the diﬃculties to value them. As a result, intangible assets are

not properly accounted for in corporate balance sheet (Hunter et al. 2005).6

These issues amount to signiﬁcant agency costs, due to adverse selection and moral

hazard concerns.7 Such costs are likely to be passed on to borrowers, resulting in less

4An intangible asset is deﬁned as "a claim to future beneﬁts that does not have a physical or ﬁnancial
[...] embodiment" (Lev 2000). They are generally organized in three broad categories: computerized
information, innovative property and economic competencies (Corrado et al. 2005).

5Innovation process implies sunk costs and failures. Using patent data, Stevens and Burley (1997)

argue that out of 3000 raw ideas, 1 turns out to be a commercial success.

6Banks can obviously rely on more information than balance sheet data, but inadequate accounting

frameworks means a higher screening cost for banks.

7Agency costs are used in a broad sense in this paper, encompassing all contracting, or transaction
costs, screening costs and moral hazard costs (Meckling and Jensen 1976, Smith 1989). They include all

5

favourable contract terms. These problems are compounded by the diﬃculty to collat-

eralize intangible assets, due to non-separability and transferability, limiting the possi-

bility for creditors to recover their funds in case of bankruptcy. All these factors reduce

access to external ﬁnance for investment in intangible assets, relative to tangible assets.

Given this possibility of substantial ﬁnancial frictions when accessing external ﬁ-

nance, one would expect that investment in intangibles is highly dependent on inter-

nal ﬁnance. However, an important strand of empirical literature, which focuses on the

sensitivity of R&D to cash ﬂows, provides mixed evidence (see Hall and Lerner 2010

for a review). Two arguments have been put forward to explain this puzzle. First, the

econometric problem is diﬃcult to tackle because of possible high adjustment costs of

intangibles (Hall and Lerner 2010, Brown et al. 2012, Peters and Taylor 2017). Knowl-

edge assets are mostly embodied in persons, who cannot be hired or ﬁred without sub-

stantial costs, especially when knowledge is tacit and ﬁrm-speciﬁc. Because ﬁrms an-

ticipate both these high adjustment costs and the possibility of facing external ﬁnance

constraints, they are likely to invest in intangibles only when they are certain of being

able to cope with temporary adverse ﬁnancial conditions. As a result, both high and

low sensitivity of intangible investment to cash ﬂows can be interpreted as evidence of

ﬁnancial frictions, preventing easy identiﬁcation. Second, Kaplan and Zingales (1997)

strongly criticized the soundness of cash-ﬂow-based analyses. In their seminal paper,

the authors demonstrate using ﬁrms’ own assessments that an important share of ﬁrms

with high cash ﬂow sensitivity were actually not ﬁnancially constrained. This conclu-

sion spurred a large strand of literature conﬁrming their results (Alti 2003, Cleary et al.

2007). These two important issues led to conﬂicting results regarding the impact of ﬁ-

nancial constraints on R&D expenditures, the most studied intangible asset (Brown

et al. 2012).

Loan contracts have been studied extensively in the theoretical literature, in partic-

ular through the lens of agency theory. In this framework, the lender and the borrower

are in a principal-agent relationship in which the borrower (agent) may have informa-

tion unknown to the lender (principal). More speciﬁcally, the literature has focused

costs that result from the willingness of a principal (lender) to contract with an agent (borrower) in a
setting with possibly conﬂicting objective functions and information asymmetries.

6

on cases where the borrower has private information on his own characteristics/type

(i.e. adverse selection or hidden-knowledge model) or his action (i.e. moral hazard

of hidden-action model). These information asymmetries have important implications

for the design of loan contracts. To limit the costs of adverse selection and moral hazard

(i.e. agency costs) the lender has to oﬀer contract terms that elicits borrower’s private

knowledge of his type and induces the borrower to act in the principal’s best interest.8

In their seminal paper, Stiglitz and Weiss (1981) document that each of these infor-

mation asymmetries can lead to equilibrium credit rationing in the loan market. They

demonstrate this result by showing that with adverse selection or moral hazard, banks’

proﬁt is not a monotonic function of the lending rate. Increasing interest rates above a

certain threshold can decrease banks’ expected returns. This upper bound on interest

rates means that in equilibrium banks can set a rate which is lower than the market-

clearing rate, resulting in an excess demand for credit. This implies that some loan

applicants are denied loans in equilibrium even though they are willing to pay an in-

terest rate above the one charged by banks to identical borrowers who did obtain a loan.

Following this important breakthrough, a stream of work has extended this framework

to evaluate whether equilibrium credit rationing could occur with more complex loan

contracts. In particular, Bester (1985, 1987) shows that banks can use collateral require-

ments in addition to interest rates to attenuate adverse selection and moral hazard, and

thereby limit credit rationing. Similarly, contract with diﬀerent maturities can also help

mitigating information asymmetry issues as shown by Ortiz-Molina and Penas (2008).9

Given these diﬀerent ways of addressing adverse selection and moral hazard, De Meza

and Webb (2006) question whether equilibrium credit rationing a la Stiglitz and Weiss

(1981) could be observed at all in the data. They demonstrate that if collateral re-

quirements and interest rates prove insuﬃcient to fully control asymmetric informa-

tion, lenders can always provide loans of smaller size which are proﬁtable to both par-

ties instead of denying credit altogether. They conclude that this type of quantity ra-

8If both parties have the same information (i.e. symmetric information), the lender can always oﬀer
a contract that perfectly controls the agent, thereby eliminating any principal-agent issues (Laﬀont and
Martimort 2002)

9Freixas and Rochet (2008) provide a comprehensive analysis of credit rationing a la Stiglitz and Weiss

(1981) and how it can be alleviated.

7

tioning, where borrowers are granted a smaller loan than requested, is more likely to

be observed than pure credit rationing a la Stiglitz and Weiss (1981), where borrowers

are randomly denied loans because of excessive demand.10 In this paper, we focus on

the impact of quantity rationing on investment rather than Stiglitz and Weiss’ credit

rationing at the loan approval stage.11

To understand how exactly agency costs can impact the diﬀerent loan terms, it is

important to understand the loan contracting process. Using detailed data on German

loans, Kirschenmann and Norden (2012) indicate that ﬁrms ask for a speciﬁc amount

and maturity when applying for a loan, then banks negotiate collateral requirements,

and both sides eventually agree on the loan rate. Based on this observation and the

theoretical results mentioned above, one would expect that as long as the lender can

negotiate the adequate collateral and rate such as to limit the impact of asymmetric

information, borrowers should not face any constraint on the amount and maturity

initially requested.12 This means that quantity rationing or constraints on the maturity

of the loan are more likely to be observed for ﬁrms with relatively high agency costs,

which cannot be suﬃciently attenuated with any combination of interest rate and col-

lateral requirements by the lender. Hence, an increase in agency costs not only has an

impact on the probability of being dissatisﬁed with at least one of the loan terms (i.e.

the probability of being constrained), it also has an impact on which exact loan term

the ﬁrm is most likely to be dissatisﬁed with. In other words, conditional on being

dissatisﬁed with at least one dimension, riskiest borrowers are more likely to be dissat-

isﬁed with the loan amount than safer ﬁrms. As a result, we expect ﬁrms with diﬀerent

agency costs to face diﬀerent loan terms, bringing about diﬀerent ﬁnancial constraints.

Several papers have studied the impact of quantity rationing on intangible invest-

ment, we contribute to this literature by evaluating the eﬀect of other important loan

10Stiglitz and Weiss’s credit rationing is sometimes referred to as credit rationing of type II, while the
other form of rationing, in which agents get a smaller loan than requested, is deﬁned as type I and was
initially studied by Jaﬀee and Russell (1976). The distinction between these two types of rationing was
originally made by Keeton (1979).

11EIBIS does provide some information on whether a ﬁrm was rejected or not when applying for a
loan, but it remains scarce. As more waves of the survey become available, comparing the eﬀects of
these two types of rationing should become feasible.

12Clemenz (1986) formally shows that quantity rationing should not be observed in equilibrium as

long as borrowers have enough assets to pledge as collateral.

8

contract terms. Due to banks’ diﬃculties to cope with asymmetric information in the

case of intangible investment, relatively safer ﬁrms, which are not quantity constrained,

may still be aﬀected by high collateral requirements or higher than expected borrowing

rates.13

The objective of our study is to evaluate the impact of loan conditions, deﬁned by

the degree of ﬁrms’ satisfaction with their loan terms, on investment in intangible as-

sets. The identiﬁcation of this causal eﬀect is made diﬃcult by the endogeneity of loan

conditions. First, as discussed above, it is likely that the nature of investment impacts

loan conditions, implying a reverse causality issue. Indeed, ﬁrms investing in intan-

gibles are more likely to face inadequate loan conditions due to, among other things,

the hardship to properly value intangible assets and the diﬃculty to pledge these as-

sets. Second, some ﬁrm-speciﬁc unobservable characteristics can have an impact on

both loan conditions and the decision to invest, such as the willingness to undertake

investment in intangible assets (i.e. self-selection bias), or the uncertainty associated

with the investment output (Savignac 2008).14 Consequently, we implement an instru-

mental variable approach to deal with these endogeneity concerns. Following Mancusi

and Vezzulli (2014), we use the external ﬁnance constraint index developed by Whited

and Wu (2006) as an instrument for our survey-based dissatisfaction measures.

Whited and Wu (2006) derive the Euler equation of a standard intertemporal invest-

ment model augmented to account for ﬁnancial frictions. Their model’s Euler equation

includes the shadow cost of external ﬁnance, i.e. the cost associated with raising ex-

ternal ﬁnance, which is assumed to be costlier than internal ﬁnance. This shadow cost

is unobservable. As a result, they parametrize this cost as a function of ﬁrm observ-

able characteristics and estimate the resulting complete Euler equation. This allows

them to derive the parameters of the external ﬁnance cost function, that they deﬁne as

13The impact of agency costs on tangible investment has been studied by many, starting with Bernanke

and Gertler (1989).

14As more waves of the EIBIS will be conducted, future research will be able to deal with some of

these unobservable eﬀects using panel data techniques

9

a ﬁnancial constraint index. The ﬁnal WW index reads as follows:

W Wit = − 0.091 CFit − 0.062 DIV P OSit + 0.021 T LT Dit

(1)

− 0.044 LN T Ait + 0.102 ISGit − 0.035 SGit

where CF represents the cash ﬂow to total assets ratio; DIV P OS is an indicator that

takes the value of one if the ﬁrm pays cash dividends15; T LT D is the ratio of long-term

debt to total assets; LN T A is the logarithm of total assets; ISG is the ﬁrm’s industry

sales growth and SG represents ﬁrm sales growth.16 A higher value of W W implies

tighter credit conditions.

Other indices of ﬁnancial constraints based on ﬁnancial characteristics have been

used in the literature (Silva and Carreira 2012). A popular alternative to the WW index

is the index based on the work of Kaplan and Zingales (1997). This index is presented

by Lamont and Polk (2001) as the "KZ index". However, Hadlock and Pierce (2010)

provide evidence that the latter is not robust. They show that the WW index is not

fully robust in their sample, too, but has better predictions than the KZ index.17 In

the following, we are able to show that the WW index is a signiﬁcant and important

determinant of ﬁrms’ dissatisfaction with loan terms in our sample, strengthening our

choice to use it as an instrument.

3 Data and Stylized Facts

Our analysis is based on the data provided by the EIBIS. The EIBIS is an annual sur-

vey of non-ﬁnancial ﬁrms in the EU that aims at monitoring investment and invest-

ment ﬁnance activities in order to capture potential barriers to investment. The survey

started in 2016 and includes some 12,500 completed interviews every year.18 Using

15We do not have in the EIBIS the distinction between cash and stock dividends. Therefore we use an

indicator variable that indicates whether the ﬁrm pays any type of dividend (i.e. cash or stock)

16Industry sales growth is deﬁned at the three-digit SIC level in Whited and Wu (2006). The EIBIS

provides a more aggregated classiﬁcation, with four distinct sectors.

17Hadlock and Pierce (2010) provide their own indicator of ﬁnancial constraint based on size and age
of the ﬁrm. We do not use this index as an instrument, given that both these variables are also used as
determinants of intangible investment.

18A share of ﬁrms are interviewed over several years, which will render possible to use the time di-

mension in further analyses when more waves will be available.

10

a stratiﬁed sampling methodology, the EIBIS is representative across all 27 EU Mem-

ber States and the UK. The representativeness relates to four ﬁrm size classes (micro,

small, medium and large) and four broad sector groupings (manufacturing, services,

construction and infrastructure) within countries.19 EIBIS respondents are sampled

from the Orbis database of Bureau van Dijk and, as a result, survey answers can be

matched to ﬁrm balance sheet and proﬁt-and-loss data provided in Orbis.

In the following analysis, we employ three waves of the survey: 2016, 2017 and

2018. We focus on the sub-sample of ﬁrms that declared having used bank ﬁnancing

for their most recent investment.20 In order to assess ﬁrms’ credit conditions, we use

the information provided in the EIBIS regarding ﬁrms’ satisfaction with bank ﬁnance.

Firms are explicitly asked to indicate their degree of satisfaction with four dimensions

of the external ﬁnance they obtained: amount, cost, maturity and collateral require-

ments.21 This is a diﬀerent measure from the one used in a closely related paper by

Mancusi and Vezzulli (2014), which is based on a survey of Italian ﬁrms. In the survey

they use, respondents are asked whether they desired or not additional bank ﬁnanc-

ing at the agreed interest rate. Consequently, as they explain, their credit constraint

variable measures the degree of quantity rationing. The advantage of EIBIS is that it

contains information on several other terms of the loan contract, including collateral

requirements and maturity, which are key for investment in intangible assets.

In addition to their investment ﬁnancing, ﬁrms are also asked to detail the nature

of their investment. Following European Investment Bank (2018), we deﬁne as intan-

gible investment the following categories: i) research and development - including the

acquisition of intellectual property (denoted here as R&D); ii) software, data, IT net-

works and website activities (software); iii) training of employees (training) and iv)

organisation and business process improvements (organisational capital).22 Conse-

19The infrastructure sector in EIBIS comprises ﬁrms from NACE Rev.2 sectors Electricity, gas, steam
and air conditioning supply (D); Water supply; sewerage, waste management and remediation activities
(E); Transportation and storage (H); Information and communication (J).

20Firms are asked the type of external ﬁnance they used and may choose among several types, we

keep those that picked "bank loans" and "other types of bank ﬁnancing".

21For each dimension of external ﬁnance (i.e. amount, cost, maturity and collateral) ﬁrms are asked
whether they are i) very satisﬁed, ii) fairly satisﬁed, iii) neither satisﬁed nor dissatisﬁed, iv) fairly dis-
satisﬁed or v) very dissatisﬁed.

22In the EIBIS, ﬁrms are asked about the amount invested in each of these intangible asset categories.
They are also asked the amount invested in tangible assets, which comprise land, business buildings,

11

quently, we are able to capture a broad range of intangible investment activities that are

not captured in previous studies that usually focus on R&D. One important exception

is Popov (2014) who investigates the impact of bank credit conditions on investment

in on-the-job training in transition economies. In our analysis, we are able to have a

broader view and look at the impact of access to credit on the total amount invested in

intangibles, which is important given the complementarities of these assets. Tables 2

and 3 present the main variables and descriptive statistics.

We ﬁrst investigate the distribution of intangible investment in our sample across

the four categories of assets available in the data. In panel (a) of Figure 1 we observe

that software and training are the most frequent types of investment with 67% of ﬁrms

investing in the former and 70% in the latter. On the other hand, 32% of ﬁrms invest

in organisational capital and 24% in some form of R&D. Panel (b) shows the mean

share invested in each intangible in our sample. Investment in software constitutes

the highest share, amounting to 13% of total investment, while organisational capital

and R&D investments represent 6% of total investment on average. The sum of the

shares, 32% in our sample, indicates that investment in intangible assets is around half

of investment in tangible assets.

We investigate whether bank loan policies have an eﬀect on both the probability to

invest and the amount invested.

infrastructure, machinery and equipment.

12

Figure 1: Breakdown of Intangible Investment

(a) % of ﬁrms

(b) Mean % invested

Note: The x-axis in panel (a) represents the share of ﬁrms that invest in each intangible
category, in per cent. Panel (b) is the mean amount invested in each intangible category as a
share of total investment (i.e. tangible and intangible investment), in per cent.

In our sample, only 30% of ﬁrms that invest in intangibles assets do so in a single

asset type (Figure 2), i.e. most ﬁrms use a combination of intangibles to maximize

the return on their investment. The idea of complementarities across intangible assets

has been discussed in the literature (Crass and Peters 2014, Thum-Thysen et al. 2017).

Consequently, we also investigate whether loan terms have a negative impact on the

likelihood to invest in a diversiﬁed portfolio of intangible assets. Such a negative eﬀect

would mean that dissatisfying credit conditions undermine not only the probability

to invest in intangibles, but also the possibility to maximize the beneﬁts of synergies

across assets.

Considering the breakdown across the diﬀerent categories of intangibles, we doc-

ument in panel (b) of Figure 2 that ﬁrms using bank lending represent 38% of total

R&D spending in the sample, 53% of total software expenditures, 28% of total training

expenditures and 63% of total organisational capital investment. These diﬀerences sug-

gest that bank credit conditions may diﬀer depending on the type of intangible asset

the ﬁrm invests in.

Regarding their credit access conditions, ﬁrms are questioned about their satisfac-

tion with four dimensions of their loan contract: amount, cost, maturity and collateral

requirements. We report in Figure 3 that, among the ﬁrms that declare being dissatis-

13

32706724020406080R & DSoftwareTrainingOrganisational Capital610136051015R & DSoftwareTrainingOrganisational CapitalFigure 2: Variety of intangible investment and ﬁnancing

(a) % of investing ﬁrms
by number of assets

(b) % of bank ﬁnance

Note: Panel (a) represents the percentage of ﬁrms that invest in 1,2,3 or 4 diﬀerent intangible
assets. The four categories of intangible investment are i) research and development -
including the acquisition of intellectual property; ii) software, data, IT networks and website
activities; iii) training of employees and iv) organisation and business process
improvements. Panel (b) gives the total invested in a given intangible by ﬁrms using bank
lending as a percentage of the total amount invested in this intangible in the full sample (i.e.
ﬁrms using bank ﬁnancing and ﬁrms using other forms of ﬁnancing).

ﬁed with at least one dimension, most ﬁrms (64%) are actually dissatisﬁed with one

dimension only. Among these ﬁrms, we ﬁnd that 47% are dissatisﬁed with collateral

requirements, 35% with the cost, 9% with the maturity and 8% with the amount. This

suggests that, when granting the loan, banks primarily account for credit risk using

collateral and rate, and rarely adjust the amount and maturity requested by borrow-

ers.23

Such practicies provide support to agency theories predicting that lenders use both

interest rates and collateral requirements to deal with asymmetric information regard-

ing borrowers’ credit risk (e.g. Strahan 1999). Looking more closely at these loan terms,

we observe a negative relationship between dissatisfaction with collateral requirements

and interest rate, indicating some degree of substitutability (Table 1). This substi-

tutability is in line with the predictions of the literature regarding how both terms can

be used by lenders to elicit borrowers’ type and thus limit adverse selection. In partic-

ular, Bester (1987) shows that borrowers with a relatively higher probability of default

prefer contracts with higher interest payments and lower collateral requirements than

23Kolev et al. (2019) use the EIBIS to analyze how bank ﬁnancial strength accounts for borrowers’

satisfaction with their access to credit.

14

30342313010203040% of firms1234Number of intangible asset types632853380204060R & DSoftwareTrainingOrganisational Capitalborrowers with lower default risk. The existence of such contracts would explain the

negative correlation observed in our data.

Table 1: Correlation Matrix of Dissatisfaction Indicators

AmountD CostD MaturityD CollateralD

AmountD
CostD
MaturityD
CollateralD

100
9.9∗∗∗
12.3∗∗∗
-4.1

100
-3.5
-33.5∗∗∗

100
-6.8∗∗

100

Note: Pairwise Pearson correlation coeﬃcients for the sample of ﬁrms
that indicates being dissatisﬁed with at least one dimension of their
loan (%). *, **, *** indicate signiﬁcance at the 10%, 5% and 1% level,
respectively.

Figure 3: Dissatisfaction with Loan Terms

Note: Percentage of ﬁrms that report being dissatisﬁed with one dimension only,
two dimensions, three dimensions or all dimensions (i.e. four) of their external
ﬁnance; among the ﬁrms that declare being dissatisﬁed with at least one
dimension. The four dimensions reported in the EIBIS are: amount, cost, maturity
and collateral requirements.

4 Empirical Model

We ﬁrst develop a model to evaluate the eﬀect of loan conditions on the probability to

invest in intangibles. To this end, we deﬁne the following system:






intanDT

it = 1[α · dissatisﬁedit + Xitβ + uit ≥ 0],

dissatisﬁedit = 1[Xitδ + Zitγ + vit ≥ 0],

(2)

15

6422950204060% of firms1234Number of loan terms the firm is dissatisfied with where intanD equals one if the ﬁrm i invests in intangible asset T . Depending on

the analysis, T may represent any type of intangible, i.e. intanD equals one if ﬁrm i

invests in any intangible, or a speciﬁc intangible type among the four categories that we

consider: i) R&D (including the acquisition of intellectual property); ii) software, data,

IT networks and website activities; iii) training of employees or iv) organisation and

business process improvements. The variable dissatisﬁed is a dummy that represents

the dissatisfaction of ﬁrms with loan terms. Hence, dissatisﬁed is equal to one if a ﬁrm

is "fairly dissatisﬁed" or "very dissatisﬁed" with at least one of the dimensions of its

bank loan – amount, cost, maturity or collateral.

The vector X includes the variables used in related studies as determinant of cor-

porate investment in intangible assets. Most of these variables come from the literature

investigating the determinants of R&D expenditures. Our baseline speciﬁcation con-

trols for the company’s age, which is likely to inﬂuence both loan conditions and in-

vestment in intangibles. Older ﬁrms may beneﬁt from more adequate loan conditions

due to longer relationships with banks and lower risk of default (Hadlock and Pierce

2010). On the other hand, the return on intangible assets, such as R&D, may be higher

for young ﬁrms than older ﬁrms with established products in the market (Mancusi and

Vezzulli 2014). Similarly, we include in our baseline model a dummy to identify small

ﬁrms, with less than 50 employees, which do not have the same propensity to invest

in intangibles, such as R&D or on-the-job training (Popov 2014), and may face spe-

ciﬁc ﬁnancial constraint due to their lack of physical capital (Cook et al. 2003, Jiménez

et al. 2017). We include two dummies to identify ﬁrms that are independent, i.e. not a

subsidiary, and foreign owned, as these characteristics can aﬀect ﬁrms’ access to credit

and their investment strategies. Finally, we include a full set of country, industry and

year dummies to control for the operational environment. This aims at accounting for

possible heterogeneities in technological opportunities, or technology push, and de-

mand pull (Savignac 2008). It also accounts for the diﬀerences in regulatory regimes

and local policies that may be relevant for intangible investment, for example R&D tax

credits or labor regulations for on-the-job training.

The variable Z is the instrumental variable that we use to address the endogeneity

16

of our main explanatory variable – the credit constraint indicator. Following Savignac

(2008) and Mancusi and Vezzulli (2014), we use selected ex ante ﬁrms’ ﬁnancial ratios

to instrument their external ﬁnance constraint. More speciﬁcally, we compute the index

of ﬁnancial constraint built by Whited and Wu (2006), referred to as the WW index, and

use it as an instrument for ﬁrms’ loan conditions (Mancusi and Vezzulli 2014).

We estimate model 2 using a recursive bivariate probit by specifying that error terms

are independently and identically distributed as bivariate normal:





uit

vit







 ∼ N










0
,
0



1 ρ



ρ 1



 .

Model 2 is coherent and complete, as deﬁned by Lewbel (2007), and can be estimated

using a full-information maximum likelihood approach (Greene 1998).

Having estimated the impact of loan terms on the likelihood to invest in intangibles,

we evaluate the eﬀect of loan terms on the amount invested in intangibles. In order to do

so, we deﬁne the following Tobit model that includes the binary endogenous regressor

dissatisﬁed:





intanAT ∗

it = α · dissatisﬁedit + Xitβ + uit,

dissatisﬁedit = 1[Xitδ + Zitγ + vit ≥ 0],

(3)

where intanAT ∗

it represents the (unobserved) logarithm of the latent amount of the

ﬁrm’s investment in intangibles. The observed variable intanAT

it is deﬁned as follows:

intanAT

it =




intanAT ∗
it

if intanAT ∗

it > 0



0

if intanAT ∗

it ≤ 0

,

(4)

i.e. the ﬁrm invests only when the optimal latent amount is positive. As shown in

Maddala (1983), this model can be estimated using maximum likelihood when error

terms u and v are multivariate normal with mean 0 and covariance:

17

Σ =





σ2
u σuv

σuv σ2
v



 .

The next section presents the results of these estimations and discusses their implica-

tions.

5 Results

Table 4 displays the results obtained regarding the impact of loan conditions on ﬁrms’

decision to invest in intangibles. In column 1, we perform a simple probit model, with-

out accounting for the endogeneity of the dissatisfaction indicator. We obtain a positive

and signiﬁcant coeﬃcient of the credit constraint measure (i.e. dissatisﬁed) on the prob-

ability to invest in any intangible asset. This result is in line with the ﬁndings of pre-

vious studies that investigate the eﬀect of ﬁnancial constraints on R&D investment or

innovation (Savignac 2008, Mancusi and Vezzulli 2014), which also ﬁnd a counterintu-

itive positive eﬀect of diﬀerent measures of credit constraints on investment. As detailed

above, this is due to the endogeneity of the constraint indicator. We tackle this issue by

instrumenting the dissatisfaction measure with the WW index. Using this instrument,

we estimate the bivariate probit model deﬁned in equation 2. Results are displayed in

columns 2 and 3. In column 2, we present the coeﬃcients of the bottom equation of the

bivariate probit model, i.e. the determinants of the dissatisfaction index. First, it can be

noticed that the instrument, shown at the bottom of the table, is a signiﬁcant determi-

nant of the likelihood of being dissatisﬁed with loan conditions (a higher value of the

WW index as deﬁned in equation 1 implies tighter ﬁnancial constraints). Second, the

correlation coeﬃcient across residuals from the two equations of the model, i.e. ρ, is

positive and signiﬁcant, conﬁrming the endogeneity of the constraint index. Column

3 displays the average marginal eﬀects obtained for the top equation of the bivariate

probit model, which indicates the impact of loan conditions on the probability to in-

vest in intangibles. This time, the impact of the dissatisfaction index is signiﬁcantly

negative, which is in line with theoretical predictions that hampered access to bank

18

credit should hinder the probability to invest in intangibles. We observe that a ﬁrm

dissatisﬁed with its loan terms is 12% less likely to invest in intangibles. To test the

validity of our instrumental variable, we estimate the same system using the two-stage

least squares methodology and compute the eﬀective F statistic developed by Olea and

Pﬂueger (2013). The latter is above the critical value of 10 suggested by Andrews et al.

(2018), conﬁrming the validity of our approach (Table A1).

We take full advantage of the EIBIS by investigating whether diﬀerent loan condi-

tions have diﬀerent impact on investment. In particular, we are interested in separating

the eﬀect of quantity rationing, which has been studied in previous analyses, from the

impact of the remaining terms of the contract, usually not available in survey data.

As explained previously, lenders might be able to provide loans without quantity ra-

tioning by varying interest rates and collateral requirements. We are interested in test-

ing whether the loan terms needed to avoid such rationing can also have a negative

impact on ﬁrms’ investment. In order to do so, we deﬁne a new dissatisfaction index,

which takes the value 1 if the ﬁrm is dissatisﬁed with the rate, maturity or collateral re-

quirement of the loan, and 0 otherwise. Compared to previous regressions, we do not

deﬁne as constrained ﬁrms that declare that they are unsatisﬁed with their loan size.

We estimate the corresponding bivariate probit model, using the same instrument as

before, and present the results in columns 4 and 5. The signiﬁcant coeﬃcient associ-

ated to the WW index in column 4 conﬁrms the relevance of our instrument in this

new speciﬁcation. Column 5 displays the average marginal eﬀects of the determinants

of the likelihood to invest in intangibles, when accounting for the endogeneity of the

newly deﬁned dissatisfaction index. Interestingly, the impact of the ﬁnancial constraint

in this case turns out to be insigniﬁcant. Hence, the comparison of these two models

suggests that the amount is a key loan term when it comes to the decision to invest in

intangibles.

Having provided new insights on the relative importance of diﬀerent loan terms,

we turn to the relative importance of loan conditions for each type of intangible invest-

ment. Table 5 presents the results of the bivariate probit estimates when the dependent

variable represents the probability to invest in each class of intangible assets. More pre-

19

cisely, columns 1 and 2 display the coeﬃcients obtained when the dependent variable

is deﬁned as an indicator variable that takes the value 1 if the ﬁrm invests in R&D (and

possibly other intangible categories), and 0 if it does not. Similar indicator variables

are built for the decision to invest in software (columns 3-4), training of employees

(column 5-6) and organisational capital (7-8). The WW index is a signiﬁcant determi-

nant of the probability of being dissatisﬁed with loan conditions in each speciﬁcation.

Results show that unfavorable bank terms aﬀect negatively the ﬁrm’s probability to

invest in most types of intangibles. The detrimental eﬀect of ﬁnancial constraints on

the likelihood to invest in R&D and training of employees conﬁrms previous studies

that also use direct measures of ﬁnancial constraints. Interestingly, we do not detect a

signiﬁcant impact of loan conditions on the likelihood to invest in organisational cap-

ital. This result is coherent with the stylized fact presented in panel (c) of Figure 1.

Indeed, we show that a signiﬁcant share of investment in organisational capital (63%)

is undertaken by ﬁrms that use bank ﬁnancing. This suggests that ﬁrms are able to ﬁ-

nance investment in this type of intangible with loans at satisfactory terms. This result

is interesting given that organisational capital is thought as a ﬁrm-speciﬁc intangible

asset that is non-tradable and diﬃcult to value.

We document an opposite pattern regarding investment in software. Even though

a substantial share of investment in this asset category (53%) is ﬁnanced with bank

credit, the eﬀect of the credit constraint indicator is signiﬁcantly negative. One ex-

planation could simply be that the total amount invested in these two types of assets

diﬀer quite signiﬁcantly. Looking at panel (b) of Figure 1, we document that, while

organisational capital represents 6% of ﬁrms’ total investment on average, investment

in software and related assets represents more than twice this share (13%). This dif-

ference could partly explain why loan conditions impact more signiﬁcantly investment

in software than in organisational capital. However, further research is needed to fully

understand these diﬀerences and provide insights on how to relax the ﬁnancial con-

straints associated with each asset class.

An important feature of intangible assets is the existence of complementarities, i.e.

the productivity of a given intangible asset can be increased through investment in

20

another intangible. In line with this idea, we showed previously that most ﬁrms actu-

ally invest in at least two categories of intangibles simultaneously. Ballot et al. (2006)

provide evidence of a signiﬁcant positive complementarity eﬀect between training and

R&D using micro-level data for France and Sweden. Focusing on German companies,

Crass and Peters (2014) also document synergies between training and R&D expendi-

tures, along with signiﬁcant complementarities between marketing expenditures and

R&D. Given these patterns, it is important to have information on the impact of ﬁ-

nancial constraints not only on the likelihood to invest in intangibles, but also on the

probability to invest in diversiﬁed intangible assets simultaneously.

Our data allow us to provide new insights on this issue. We deﬁne a new dependent

variable, IntanDM , which is a dummy equal to one whenever the ﬁrm has invested in

two or more intangible assets, and zero otherwise. We estimate the bivariate probit

model deﬁned in equation 2 with this new dependent variable. Results are displayed

in Table 6. We show in columns 1 and 2 that loan conditions have a signiﬁcant detrimen-

tal impact on the probability of having a diversiﬁed portfolio of intangible assets. The

average marginal eﬀects, presented in column 2, suggest that ﬁrms dissatisﬁed with

the amount, cost, maturity and/or collateral are 30% less likely to invest in multiple

intangible assets. In columns 3 and 4, we run the same model but using the previously

deﬁned alternative dissatisfaction index that only accounts for dissatisfaction with the

cost, maturity and collateral requirements of the loan, excluding the amount. Inter-

estingly, compared to our previous results, we show that in addition to the amount,

other loan terms have a signiﬁcantly negative eﬀect on the probability to invest in di-

versiﬁed intangible assets. This means that restrictive rate, maturity and/or collateral

requirements hinder the possibility for ﬁrms to beneﬁt from the complementarities of

intangibles.

What can explain that the indicator measuring dissatisfaction with cost, maturity

and/or collateral requirements only have a signiﬁcant impact when we focus on invest-

ment in diversiﬁed assets? According to standard agency theory, quantity rationing is

less likely when agency costs are low enough such that lenders can mitigate informa-

tion asymmetries using diﬀerent maturities and/or diﬀerent collateral requirements

21

Figure 4: Density Estimates of the Whited-Wu Index

Note: Density estimates of the Whited-Wu index using the Epanechnikov kernel
for ﬁrms that invest in at least one intangible (solid line) and ﬁrms that invest in at
least two intangibles (dotted line).

(Ortiz-Molina and Penas 2008, Bester 1987). It simply means that if ﬁrms have health-

ier ﬁnancials (i.e. agency costs are lower), banks are more likely to be able to oﬀer

contracts without having to cut the requested loan size. Hence, when dissatisﬁed with

their loan contract, healthier ﬁrms are more likely to be dissatisﬁed with the cost, ma-

turity and/or collateral requirement than the amount. Consequently, if ﬁrms investing

in multiple types of intangible assets have typically stronger ﬁnancials, then we could

consider this as an explanation for the more signiﬁcant impact of dissatisfaction with

cost, maturity and/or collateral requirements in this case . To verify this assumption,

Figure 4 plots the kernel density estimates of the WW index for diﬀerent samples of

borrowers. The solid line represents the density of the WW index for all ﬁrms that in-

vest in at least one intangible and the dotted line represents the density of the index

only for the subgroup of ﬁrms that invest in at least two intangibles. In line with our

initial hypothesis, we observe that the density is shifted to the left for ﬁrms that invest

in at least two intangible assets, indicating healthier balance sheets.

It remains to verify that borrowers are indeed more likely to be able to avoid quan-

tity rationing when they have stronger ﬁnancials. To do so, we compute the share of

ﬁrms dissatisﬁed with (at least) their loan amount for each quartile of the distribution

of the WW index (Figure 5). More precisely, for each quartile of the WW index, we

22

01234Kernel Density Estimates-1-.9-.8-.7-.6-.5WW indexInvest in at least 1 intangibleInvest in at least 2 intangiblescompute the following ratio,

RW W =

Number of ﬁrms dissatisﬁed with at least their loan amount
Number of ﬁrms dissatisﬁed with at least one dimension of their loan

. (5)

As predicted by theory, we observe that dissatisfaction with loan size increases as the

balance sheet weakens. In our sample, 16% of ﬁrms with the most robust balance sheets

(i.e. the ﬁrst quartile in Figure 5) are likely to be dissatisﬁed with their loan amount

compared to 23% for the most fragile ﬁrms in the fourth quartile. These observations

suggest that, borrowers who invest in diversiﬁed intangible assets are less likely to be

impacted by inadequate loan size than by inadequate cost, maturity and/or collateral

requirements due to their healthier balance sheets.

Figure 5: Dissatisfaction across the Distribution of the Whited-Wu Index

Note: For each quartile of the distribution of the Whited-Wu index, percentage of
ﬁrms dissatisﬁed with (at least) the amount of their loan vs the percentage of ﬁrms
dissatisﬁed with their loan’s interest rate, maturity and/or collateral requirements
(see equation 5). The ﬁrst quartile represents ﬁrms with the lowest Whited-Wu
index, i.e. the healthiest ﬁrms, whereas the fourth quartile groups ﬁrms with the
highest Whited-Wu index, i.e. the weakest ﬁrms in terms of ﬁnancials.

We next turn to the analysis of the eﬀect of ﬁnancial constraints on intangible invest-

ment intensity. Instead of considering an indicator variable representing the decision

to invest or not in a given intangible, i.e. the propensity to invest, we now deﬁne as

dependent variable the logarithm of the amount invested. This distinction between

propensity and intensity was also made by Bond et al. (2005) and Mancusi and Vez-

23

2377247622781684020406080100% of firms4th quartile3rd quartile2nd quartile1st quartileDissatisfied with amountDissatisfied with other termszulli (2014) as regards to investment in R&D. The argument for this distinction is that

investment in intangibles is geared towards expenditures on skilled workers and tacit

knowledge, which cannot be easily adjusted because of suddenly tightened external

ﬁnance conditions. As a consequence, spending on intangibles (intensity) may not be

as sensitive to quantity rationing as the decision to invest or not in intangibles (propen-

sity).

To analyze the eﬀect of loan conditions on investment intensity, we run a Tobit

model that accounts for both the left-censoring of the variable representing the log

amount invested in intangibles, and the endogeneity of our ﬁnancial constraint indi-

cators (equation 4). Results are displayed in Table 7. We observe that our instrument

is still a signiﬁcant indicator of loan conditions in this new model (columns 1 and 4).

In columns 2 and 3 we report that ﬁrms dissatisﬁed with at least one of the four di-

mensions of their loan invest signiﬁcantly less in intangibles. When we look at our

alternative variable of ﬁnancial constraints, which indicates ﬁrms dissatisﬁed with the

rate, maturity and/or collateral requirements of their loan, we observe that the eﬀect

is virtually unchanged (columns 5 and 6). This is an important diﬀerence compared

to the results obtained regarding investment propensity where introducing dissatis-

faction with the amount was necessary to observe a signiﬁcant impact. In line with

previous studies, this shows that quantity rationing has a more important eﬀect on in-

vestment propensity than on investment intensity. Our analysis completes these results

by showing that loan terms diﬀerent from the loan amount do matter for investment

intensity. Thus, putting all pieces of the analysis together, we can conclude that ob-

taining a loan of the requested amount is important for the decision to invest or not in

intangibles. In order to be able to grant the full amount however, lenders resort to con-

tracts with interest rate, maturity and/or collateral requirements that have a negative

impact on the size of the investment.

From a policy perspective, these results provide new insights regarding the imped-

iments to investment in intangible assets in Europe, which is a bank-based economic

area.

In particular, we show that lending rates, maturity and/or collateral require-

ments have a negative eﬀect on investment in multiple intangible assets, undermining

24

the possibility to exploit the complementarities of such assets. This lends support to

the existing credit guarantee schemes for ﬁrms that are unable to provide suﬃcient

collateral. The state is often the guarantor, which helps alleviate ﬁnancial constraints

and support investment in intangibles.

Another important recent development is intangible-backed lending, especially in

Asia where there are initiatives to promote standards and/or good practice for lending

against intangible assets (Brassell and Boschmans 2019). Subsidized interest rates are

also used in some countries. These policies target the diﬀerent terms of credit, which

according to our results is necessary to unlock investment in knowledge assets. The

majority of these policy instruments target R&D. For instance, initiatives regarding

intangible-backed lending focus on the role of patents. Our analysis suggests, how-

ever, that other intangibles, such as training, represent an important share of the aver-

age ﬁrm’s total investment and is negatively impacted by inadequate loan conditions.

Thus, policies to promote intangible investment should target the diversity of existing

intangibles in order to foster not only investment but also synergies.

6 Conclusions

Empirical evidence on the impact of bank ﬁnancing on corporate investment in intan-

gible assets is of critical importance, especially in bank-dominated ﬁnancial systems

such as most European ones. Previous studies show that investment in R&D, the most

studied intangible asset, is hampered by inadequate loan terms. Researchers, however,

have not been able to fully account for the role of bank loan conditions on total intan-

gible investment, because of the diﬃculties to measure credit constraints and intan-

gible assets other than R&D. Using new survey data, representative across European

countries, we are able to document the impact of four distinct bank loan dimensions

– amount, cost, maturity and collateral requirements – on four diﬀerent categories of

intangibles – R&D, including the acquisition of intellectual property, software, data, IT

networks and website activities, training of employees and organisation and business

process improvements.

25

Using an instrumental variable approach, we show that the loan amount is a key de-

terminant of the ﬁrm’s propensity to invest in intangibles. Other possibly unfavorable

loan dimensions like cost, maturity or collateral requirements, do not have a signiﬁcant

impact on the likelihood to invest in intangibles as long as the ﬁrm is granted a satis-

factory loan amount. We further show that a suitable loan amount is not suﬃcient to

ensure that ﬁrms will be able to invest in two or more types of intangible assets simul-

taneously. Diversiﬁcation of intangibles is important to beneﬁt from the complemen-

tarities of these assets, e.g. investment in R&D, coupled with training of employees.

This ﬁnding gains importance by the fact that in Europe the majority of ﬁrms investing

in intangible assets do so in more than two types of these simultaneously. This sug-

gests that the negative eﬀect of high lending rates, maturity or collateral requirements

on the probability to invest in multiple intangibles prevents ﬁrms from maximizing in-

vestment return. Finally, we document that a satisfactory loan amount is not suﬃcient

to ensure that ﬁrms invest as much as desired in intangible assets. High lending rates,

maturity or collateral requirements have a detrimental eﬀect on intangible investment

intensity.

From a policy perspective, our results ﬁrst conﬁrm that tackling the issue of access

to ﬁnance for investment in intangible assets is important, as suggested by previous

studies on ﬁnancing of R&D expenditures. These studies also show that policies aim-

ing at relaxing other loan terms are necessary in order to fully unlock investment in

knowledge assets in Europe. Consequently, policies such as subsidized loans, credit

guarantee schemes or intangible-backed lending could remove important impediments

to investment.

26

Table 2: Variable Deﬁnitions

Variable

Deﬁnition

Intangible Investment

IntanD
Intan2D
IntanA
R&D
Software
Training
Organisational Capital

Loan condition indices

Dissatisﬁed
Dissatisﬁed alt.
AmountD
CostD
MaturityD
CollateralD

Instrument

WW index

Size

Small
Ownership

Independent
Foreign

Age

=1 if invested in any intangible
=1 if invested in at least two intangible types
log amount of intangible investment
=1 if invested in R&D
=1 if invested in Software
=1 if invested in Training
=1 if invested in Organisational Capital

=1 if dissatisﬁed with at least one loan dimension
=1 if dissatisﬁed with cost, maturity or collateral but satisﬁed with amount
=1 if dissatisﬁed with amount
=1 if dissatisﬁed with cost
=1 if dissatisﬁed with maturity
=1 if dissatisﬁed with collateral requirements

Whited and Wu’s external ﬁnance constraint index

=1 if less than 50 employees

=1 if independent
=1 if foreign owned

Dummy variable
Less than 2 years
Dummy variable
2 years to less than 5 years
5 years to less than 10 years
Dummy variable
10 years to less than 20 years Dummy variable
Dummy variable
20 years or more

Sector

Manufacturing
Construction
Services
Infrastructure

Dummy variable
Dummy variable
Dummy variable
Dummy variable

27

Table 3: Summary Statistics

Intangible Investment

IntanD
Intan2D
IntanA
R&D
Software
Training
Organisational Capital

Loan condition indices

Dissatisﬁed
Dissatisﬁed alt.
AmountD
CostD
MaturityD
CollateralD

Instrument

WW index

Size

Small
Ownership

Independent
Foreign

Age

Less than 2 years
2 years to less than 5 years
5 years to less than 10 years
10 years to less than 20 years
20 years or more

Sector

Manufacturing
Construction
Services
Infrastructure

Mean

SD Min Max

N

0.91
0.71
10.78
0.33
0.72
0.76
0.40

0.21
0.17
0.04
0.10
0.04
0.12

0.29
0.45
2.34
0.47
0.45
0.43
0.49

0.41
0.37
0.21
0.30
0.20
0.33

0.00
0.00
1.65
0.00
0.00
0.00
0.00

0.00
0.00
0.00
0.00
0.00
0.00

1.00
1.00
21.08
1.00
1.00
1.00
1.00

1.00
1.00
1.00
1.00
1.00
1.00

5256
5256
4762
5256
5256
5256
5256

5378
5194
5521
5494
5514
5410

-0.73

0.10

-0.99

-0.48

5550

0.41

0.49

0.00

1.00

5550

0.43
0.06

0.00
0.03
0.10
0.24
0.63

0.33
0.19
0.23
0.24

0.49
0.24

0.05
0.16
0.30
0.43
0.48

0.47
0.40
0.42
0.43

0.00
0.00

0.00
0.00
0.00
0.00
0.00

0.00
0.00
0.00
0.00

1.00
1.00

1.00
1.00
1.00
1.00
1.00

1.00
1.00
1.00
1.00

5252
5129

5550
5550
5550
5550
5550

5550
5550
5550
5550

Note: Summary statistics of the main variables used in the diﬀerent empirical
models. The sample of banks is described in Section 2.

28

Table 4: Impact of Unsatisfying Loan Terms on the Probability to Invest in Intangibles

Probit

(1)
IntanD
Coef.

0.17∗∗
(0.069)

0.11
(0.58)
0.28
(0.57)
0.26
(0.57)
0.30
(0.57)

-0.34∗∗∗
(0.057)

0.061
(0.061)
0.29∗∗
(0.15)

0.16∗∗
(0.079)
0.20∗∗∗
(0.075)
-0.051
(0.070)

-1332.7
Yes
Yes
4693

Bivariate Probit

(2)
Dissatisﬁed
Coef.

(3)
IntanD
Marg. Eﬀ.

(4)
Dissatisﬁed
Coef.

(5)
IntanD
Marg. Eﬀ.

-0.12∗∗
(0.056)

0.033
(0.11)
0.068
(0.10)
0.065
(0.10)
0.070
(0.10)

-0.050∗∗∗
(0.0099)

0.0080
(0.010)
0.038∗∗
(0.018)

0.032∗∗∗
(0.012)
0.030∗∗∗
(0.012)
-0.011
(0.013)

Yes
Yes
4711

-0.12
(0.52)
-0.019
(0.51)
0.0018
(0.51)
-0.050
(0.51)

-0.0024
(0.059)

-0.071
(0.047)
-0.046
(0.10)

0.18∗∗∗
(0.060)
-0.040
(0.058)
-0.083
(0.059)

1.64∗∗∗
(0.31)
0.49∗∗∗
-3646.2
Yes
Yes
4711

-0.090
(0.064)

0.040
(0.11)
0.060
(0.11)
0.060
(0.11)
0.064
(0.11)

-0.053∗∗∗
(0.0099)

0.0059
(0.0099)
0.034∗
(0.018)

0.032∗∗∗
(0.012)
0.029∗∗
(0.012)
-0.010
(0.013)

Yes
Yes
4550

-0.20
(0.54)
-0.10
(0.53)
-0.077
(0.53)
-0.14
(0.53)

-0.035
(0.063)

-0.079
(0.051)
-0.12
(0.11)

0.18∗∗∗
(0.064)
-0.039
(0.062)
-0.036
(0.063)

1.43∗∗∗
(0.34)
0.39∗∗
-3273.9
Yes
Yes
4550

Loan condition indices:
Dissatisﬁed

Dissatisﬁed alt.

Age:
2 years to less than 5 years

5 years to less than 10 years

10 years to less than 20 years

20 years or more

Size:
Small

Ownership:
Independent

Foreign

Sector:
Construction

Services

Infrastructure

Instrument:
WW index

ρ
Log Lik
Country Dummies
Time Dummies
Observations

Note: Coef. stands for coeﬃcients and Marg. Eﬀ. indicates the resulting average marginal eﬀects. Log
Lik is log likelihood. Standard errors robust to heteroskedasticity. *, **, *** indicate signiﬁcance at the
10%, 5% and 1% level, respectively.

29

l
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s
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o
N

.

y
l
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v
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t
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e
p
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r

,
l
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v
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l

%
1
d
n
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%
5

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0
1

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t

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s

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*
*
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,
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,
*

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y
t
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c
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t
s
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k
s
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t
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n
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t
S
d
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h
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l
e
k
i
l
g
o
l

.

30

Table 6: Impact of Unsatisfying Loan Terms on the Probability to Invest in Multiple Intan-
gible Assets

Baseline

Alternative dissatisfaction index

(1)
Dissatisﬁed
Coef.

(2)
IntanDM
Marg.Eﬀ.

(3)
Dissatisﬁed
Coef.

(4)
IntanDM
Marg. Eﬀ.

Loan condition indices:
Dissatisﬁed

Dissatisﬁed alt.

Age:
2 years to less than 5 years

5 years to less than 10 years

10 years to less than 20 years

20 years or more

Size:
Small

Ownership:
Independent

Foreign

Sector:
Construction

Services

Infrastructure

Instrument:
WW index

ρ
Log Lik
Country Dummies
Time Dummies
Observations

-0.30∗∗∗
(0.049)

-0.092
(0.12)
-0.047
(0.11)
-0.055
(0.11)
-0.042
(0.11)

-0.10∗∗∗
(0.015)

-0.00017
(0.013)
0.012
(0.027)

-0.0077
(0.017)
-0.023
(0.016)
-0.062∗∗∗
(0.016)

Yes
Yes
4711

-0.14
(0.52)
-0.033
(0.51)
-0.016
(0.51)
-0.054
(0.51)

-0.031
(0.059)

-0.063
(0.047)
-0.0099
(0.100)

0.19∗∗∗
(0.059)
-0.036
(0.057)
-0.081
(0.058)

1.84∗∗∗
(0.30)
0.60∗∗∗
-4883.5
Yes
Yes
4711

-0.22
(0.54)
-0.12
(0.53)
-0.098
(0.52)
-0.16
(0.52)

-0.060
(0.064)

-0.075
(0.050)
-0.075
(0.11)

0.18∗∗∗
(0.064)
-0.040
(0.062)
-0.036
(0.062)

1.60∗∗∗
(0.34)
0.51∗∗∗
-4469.4
Yes
Yes
4550

-0.26∗∗∗
(0.067)

-0.098
(0.12)
-0.060
(0.12)
-0.065
(0.11)
-0.050
(0.11)

-0.12∗∗∗
(0.016)

0.0015
(0.014)
0.0049
(0.028)

-0.011
(0.018)
-0.021
(0.017)
-0.061∗∗∗
(0.017)

Yes
Yes
4550

Note: Bivariate probit models. Coef. stands for coeﬃcients and Marg. Eﬀ.
indicates the resulting
average marginal eﬀects. Log Lik is log likelihood. Standard errors robust to heteroskedasticity. *, **,
*** indicate signiﬁcance at the 10%, 5% and 1% level, respectively.

31

Table 7: Impact of Unsatisfying Loan Terms on the Amount Invested in Intangible Assets

Baseline

Alternative dissatisfaction index

(1)
Dissatisﬁed
Coef.

(2)
IntanA
Coef.

(3)
IntanA
E(Y)

(4)
Dissatisﬁed Alt.
Coef.

(5)
IntanA
Coef.

(6)
IntanA
E(Y)

Loan condition indices:
Dissatisﬁed

Dissatisﬁed Alt.

Age:
2 years to less than 5 years

5 years to less than 10 years

10 years to less than 20 years

20 years or more

Size:
Small

Ownership:
Independent

Foreign

Sector:
Construction

Services

Infrastructure

Instrument:
WW index

Log Lik
Country Dummies
Time Dummies
Observations

-5.82∗∗∗
(0.16)

-5.46∗∗∗
(0.13)

-0.61∗
(0.34)
-0.53∗
(0.32)
-0.52∗
(0.31)
-0.53∗
(0.31)

-1.64
(1.13)
-0.95
(1.07)
-0.97
(1.06)
-0.73
(1.05)

-1.62
(1.12)
-0.94
(1.06)
-0.96
(1.05)
-0.72
(1.04)

-0.23∗∗∗
(0.051)

-2.21∗∗∗
(0.15)

-2.18∗∗∗
(0.15)

-0.056
(0.043)
0.035
(0.084)

0.15∗∗∗
(0.056)
-0.016
(0.052)
-0.100∗
(0.054)

2.91∗∗∗
(0.21)

-14415.7
Yes
Yes
4711

-0.053
(0.15)
0.60∗∗
(0.27)

-0.13
(0.19)
-0.22
(0.17)
-0.56∗∗∗
(0.19)

-0.053
(0.15)
0.60∗∗
(0.26)

-0.13
(0.19)
-0.22
(0.17)
-0.56∗∗∗
(0.19)

Yes
Yes
4711

Yes
Yes
4711

-5.88∗∗∗
(0.16)

-5.49∗∗∗
(0.13)

-1.69
(1.10)
-1.16
(1.04)
-1.14
(1.03)
-0.92
(1.02)

-1.67
(1.09)
-1.14
(1.03)
-1.13
(1.02)
-0.91
(1.01)

-2.35∗∗∗
(0.15)

-2.32∗∗∗
(0.15)

-0.060
(0.15)
0.51∗
(0.26)

-0.19
(0.19)
-0.24
(0.17)
-0.50∗∗∗
(0.19)

-0.059
(0.15)
0.51∗
(0.26)

-0.18
(0.19)
-0.24
(0.17)
-0.50∗∗∗
(0.19)

Yes
Yes
4550

Yes
Yes
4550

-0.74∗∗
(0.35)
-0.62∗
(0.33)
-0.62∗
(0.32)
-0.63∗
(0.32)

-0.29∗∗∗
(0.052)

-0.058
(0.045)
0.0094
(0.091)

0.14∗∗
(0.058)
-0.015
(0.054)
-0.066
(0.057)

2.91∗∗∗
(0.22)

-13688.0
Yes
Yes
4550

Note: IV-Tobit models. Coef. are the estimated coeﬃcients of model 3 and E(Y) indicates the derived marginal
eﬀects of regressors on the actual amount invested, accounting for left-censoring (i.e. equation 4). Log Lik is log
likelihood. Standard errors robust to heteroskedasticity. *, **, *** indicate signiﬁcance at the 10%, 5% and 1% level,
respectively.

32

7 Appendix

Table A1: Impact of Unsatisfying Loan Terms on the Probability to Invest in Intan-
gibles

Baseline

Diss alt. index

(1)
First Stage

(2)

(3)

IntanD First Stage

(4)
IntanD

-0.77∗∗∗
(0.22)

0.021
(0.055)
0.088∗∗
(0.043)
0.089∗∗
(0.039)
0.081∗∗
(0.036)

0.38∗∗∗
(0.079)

-0.00073
(0.11)
0.030
(0.10)
0.032
(0.10)
0.018
(0.100)

-1.00∗∗∗
(0.32)

0.014
(0.052)
0.069∗∗
(0.032)
0.074∗∗∗
(0.025)
0.061∗∗∗
(0.019)

0.29∗∗∗
(0.074)

-0.019
(0.11)
0.0060
(0.10)
0.0097
(0.10)
-0.0059
(0.10)

0.0098
(0.016)

-0.011
(0.018)

-0.0015
(0.015)

-0.022
(0.019)

-0.019
(0.013)
-0.014
(0.024)

0.058∗∗∗
(0.018)
-0.0088
(0.016)
-0.017
(0.015)

Yes
Yes
4711

-0.0048
(0.014)
0.011
(0.024)

0.074∗∗∗
(0.023)
0.024
(0.017)
-0.029
(0.018)

22.8
Yes
Yes
4711

-0.018
(0.012)
-0.026
(0.022)

0.050∗∗∗
(0.017)
-0.0080
(0.015)
-0.0053
(0.015)

Yes
Yes
4550

-0.011
(0.017)
-0.0070
(0.028)

0.082∗∗∗
(0.027)
0.021
(0.019)
-0.021
(0.019)

15.6
Yes
Yes
4550

Loan condition indices:
Dissatisﬁed

Dissatisﬁed alt.

Instrument:
WW index

Age:
2 years to less than 5 years

5 years to less than 10 years

10 years to less than 20 years

20 years or more

Size:
Small

Ownership:
Independent

Foreign

Sector:
Construction

Services

Infrastructure

Eﬀective F (Montiel-Pﬂueger)
Country Dummies
Time Dummies
Observations

Note: 2SLS estimates. Standard errors robust to heteroskedasticity. *, **, *** indicate
signiﬁcance at the 10%, 5% and 1% level, respectively.

33

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38

ECONOMICS – WORKING PAPERS 2021/05

The impact of bank loan terms  
on intangible investment  

in Europe

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