Financing Constraints and Investment-Cash Flow Sensitivity of Cleantech Start-ups in Italy - Dossi Girasoli

Scuola di Ingegneria Industriale e dell’Informazione
Corso di Laurea Magistrale in Ingegneria Gestionale
FINANCING CONSTRAINTS AND INVESTMENT-CASH FLOW
SENSITIVITY OF CLEANTECH START-UPS IN ITALY
Relatore: Prof. Giancarlo Giudici
Correlatore: Prof. Luca Grilli
Tesi di Laurea di:
Thomas Dossi 817441
Andrea Girasoli 817417
Anno Accademico 2014/2015

Acknowledgements
Acknowledgements of Thomas and Andrea
This thesis owes its existence to the help , support and inspiration of several people.
Above all, we would like to express our sincere gratitude and appreciation to Prof. Giancarlo
Giudici for his guidance during our research. His constant support and valuable suggestions
have been precious for the development of this thesis content. His patient and extensive
involvement, at both a human and professional level, has significantly helped us to overcome
the critical moments faced during the execution of this project.
We are also grateful to Prof. Luca Grilli for his encouragements, constructive criticism and his
extensive discussions around our work.
We are extremely indebted to Prof. Annalisa Croce for her inspiring suggestions and statistical
advise, that have provided a fundamental contribution to the progress of our research.
Finally, we would like to thank Valeria Scargetta and Mattia Corbetta for generously sharing
their time and ideas, offering helpful suggestions and comments.
Acknowledgements of Thomas
I hereby would like to thank all the people who supported me during these five years.
First of all, I want to express my most sincere gratitude to my parents, who have been close to
me every step of the way with their beautiful example, their kindness and their unconditional
support. Without your help I would have never made it this far, and I must add that without
your proofreading skills this work would be quite less intelligible.
An essential thought of gratitude goes to all my grandparents, and particularly to my grandma
Annamaria, whose “fa’ giudizi!” is one of the main reasons I completed my studies.
A sincere appreciation from the deepest of my heart goes to Jackie, Rich and Kyle who, despite
a long long time has passed, never stopped caring and supporting me from the other side of
the world; I will always be grateful.
Thanks to my colleagues Marco and Federica for the great patience and availability showed
during these months, for their interest and the numerous opportunities they have given me.

My brothers shared this journey with me and had they not, these five years would have been
so much harder. Sam, thank you for setting the example and showing the way, for your
guidance, your enthusiasm and your improbable inventions. Francesco, thanks for the
countless moments of fun and for reminding me, even if not requested, that one can always
improve.
I take the chance to thank all my friends whose support and care was never weakened by time.
Niccolò, despite your stubbornness, laziness, unhealthy passion for an Old Lady and your
refusal to accept that engineering is better than physics, I must thank you for being the
greatest friend one could ask for. My deepest gratitude goes to Silvia for the joy, love and
support you have always given me, and not least for the invaluable legal advice you provided
to this work. Giulia, your happiness, craziness and never ending arguments make the world a
better place, thank you for always being around, even in the hardest times.
In these years in Milan I met some wonderful people, too many to name them all; thanks to all
of you for making the university days unforgettable. A special thought to Ale, Giu, Fabio and
Anto, who have lived these years with me from the very beginning. I must mention Edo,
teammate in a long list of projects; without your presence I probably would still have to upload
my other thesis. Thanks for all the epic moments we had, and for those to come.
Finally, a special thanks to Andrea, who took part in this venture with me. All these years you
have been a great friend and together we have faced critical situations as well as great
satisfactions. Thank you for always backing me up when needed, for passing me your notes the
few times I was late to class, and never refusing a helping hand in times of need. Looking back
at the long discussions, the rushes to meet deadlines, the afternoons studying and the late
pizzas, I now see I had the time of my life. And as I am approaching the end of this chapter, I
know I will remember all of this with nostalgic happiness.
Acknowledgements of Andrea
I would greatly like to acknowledge all of those people who have accompanied me during
these university years.
Above all, I want to express my heartfelt gratitude to my parents and my family, for constantly
helping and guiding me with their positive examples. Thank you for your support and for the
confidence you have placed in me. Without your presence it would not have been possible to
reach this ambitious goal.

My most sincere gratitude goes to all the friends with whom I have shared the best moments
during these memorable five years of my life. First of all I want to thank all the fantastic people
that I’ve met in the CdS: Dario, Peppe, Stefano, Martino, Seba, Ottla, Febb, Fede, Ciccio, Sacco,
Giona, Fabio, I’ll never forget all the beautiful moments spent together, I feel incredibly lucky
to have had the opportunity to meet you all.
A necessary thanks to all the friends that I’ve met at the university. Fabio, Ale, Giulia, Anto you
have contributed day by day to make the university experience special since the very
beginning. Thanks to Maria and Alberto, we’ve done a great job in London: mind the gap.
An essential thought of gratitude goes to Edo. Thanks for making these years unforgettable,
you are one of the craziest and most incredible people that I’ve met in my life. It has been an
honour to walk this long university path with such a sincere friend as you, and I am sure that
the best has yet to come.
I take this opportunity to thank Giorgio, Samuel and Silvia: your advices have provided a
fundamental contribution to the progress of this thesis project.
I would really like to thank Jacopo and Lorenzo, your daily support during this thesis work has
been essential, I could not ask for better housemates than you.
A special appreciation goes to the friends I grew up with. Petra, Dodo, Fred, Checca, Paolo,
Stefano, Chiara, Rossella, Graziano, Rufus, Fil, Roberto, Elisa, Deb, you are a fundamental part
of my life and I wish you all the very best for the future, being sure that we will always be there
to support each other when needed.
Last, but not least, the most sincere appreciation goes to Thomas. We have been spending
together almost every day of these five years at the university, facing an endless series of
critical moments, always succeeding in overcoming difficulties and achieving our goals with our
typical just-in-time approach. I am honoured to have shared all these experiences with such an
incredible person and a unique friend as you, and I could not have asked for a better
teammate for this last university project. I really wish you all the best in your future
endeavours and every happiness.
Andrea and Thomas, Milan, 18.12.2015

I
Table of Contents
TABLE OF CONTENTS ................................................................................................................................I
LIST OF FIGURES ....................................................................................................................................IV
LIST OF TABLES .......................................................................................................................................VI
ABSTRACT.............................................................................................................................................. VIII
ABSTRACT IN ITALIANO.......................................................................................................................... IX
EXECUTIVE SUMMARY ............................................................................................................................ X
1. FINANCING THEORIES .....................................................................................................................3
1.1 THE GENESIS OF CAPITAL STRUCTURE THEORY .................................................................................3
1.1.1 Modigliani-Miller Irrelevance Proposition ................................................................................3
1.1.2 Modigliani-Miller with Taxation ................................................................................................5
1.1.3 Miller’s Debt and Taxes Model ..................................................................................................5
1.1.4 Influence on Following Literature ..............................................................................................6
1.2 TRADE-OFF THEORY ...........................................................................................................................8
1.2.1 Static Trade-off Theory.............................................................................................................11
1.2.2 Dynamic Trade-off Theory........................................................................................................20
1.3 PECKING ORDER THEORY .................................................................................................................26
1.3.1 Theoretical Foundation of Pecking Order Theories.................................................................27
1.3.2 Adverse Selection......................................................................................................................28
1.3.3 Agency Theory ..........................................................................................................................36
1.4 FINANCING CONSTRAINTS AND INVESTMENT-CASH FLOW SENSITIVITY ..........................................41
1.4.1 Investment Theories and Financial Constraints .......................................................................41
1.4.2The Investment-Cash Flow Sensitivity Model............................................................................42
1.4.3 Influences and Applications of ICFS Theory ............................................................................46
2. START-UPS..........................................................................................................................................49
2.1 INTRODUCTION .................................................................................................................................49
2.2 IMPORTANCE OF HUMAN CAPITAL....................................................................................................50
2.3 IMPACT OF EXOGENOUS FACTORS ON NEW FIRMS FORMATION........................................................52
2.3.1 Location....................................................................................................................................52
2.3.2 Presence of Incubators .............................................................................................................55
2.4 ACCESS TO OUTSIDE FINANCINGS.....................................................................................................57
2.4.1 Determinants of the Capital Structure of Start-ups ..................................................................58
2.4.2 Angel Investors .........................................................................................................................64

II
2.4.3 Venture Capital.........................................................................................................................70
2.4.4 Crowdfunding ...........................................................................................................................85
3. THE ITALIAN LEGISLATION ON INNOVATIVE START-UPS: THE LEGISLATIVE
DECREE N. 179/2012 ..............................................................................................................................92
3.1 INTRODUCTION .................................................................................................................................92
3.2 THE LEGISLATIVE DECREE N.179/2012, SECTION IX........................................................................93
3.2.1 Article 25 ..................................................................................................................................93
3.2.2 Article 26 ..................................................................................................................................96
3.2.3 Article 27 ..................................................................................................................................98
3.2.4 Article 27-bis ............................................................................................................................98
3.2.5 Article 28 ..................................................................................................................................99
3.2.6 Article 29 ................................................................................................................................100
3.2.7 Article 30 ................................................................................................................................104
3.2.8 Article 31 ................................................................................................................................110
4. CLEANTECH.....................................................................................................................................111
4.1 CLEANTECH DEFINITION.................................................................................................................111
4.2 EMERGENCE OF CLEANTECH...........................................................................................................114
4.3 CLEANTECH INVESTMENTS .............................................................................................................118
4.4 CLEANTECH MARKET SIZE .............................................................................................................125
5. THE DATA SET.................................................................................................................................133
5.1 INTRODUCTION ...............................................................................................................................133
5.2 DATA SOURCES...............................................................................................................................133
5.3 DATA SELECTION PROCESS.............................................................................................................135
5.3.1 Cleantech Operating Definition..............................................................................................135
5.3.2 Definition of the List of Ateco 2007 Codes .............................................................................137
5.3.3 One-by-one Selection of the Cleantech Start-ups ...................................................................138
5.4 DESCRIPTIVE ANALYSIS OF THE SAMPLES ......................................................................................139
5.4.1 Temporal Trends Analysis ......................................................................................................139
5.4.2 Legal Status ............................................................................................................................144
5.4.3 Geographical Analysis............................................................................................................144
5.4 4 Analysis by Sector...................................................................................................................152
5.4.5 Analysis of Financial Figures and Economical Impact..........................................................154
6. EMPIRICAL ANALYSIS..................................................................................................................159
6.1 INTRODUCTION ...............................................................................................................................159
6.2 AIMS OF THE STUDY .......................................................................................................................159
6.3 EFFECT OF ACCESS TO PUBLIC GUARANTEE FUND ON ICFS...........................................................160
6.4 MODEL ASSUMPTIONS ....................................................................................................................163
6.5 VARIABLES .....................................................................................................................................164

III
6.5.1 Correlation Matrix..................................................................................................................165
6.6 SAMPLE AND DATA ADJUSTMENT...................................................................................................166
6.7 ECONOMETRIC SPECIFICATIONS......................................................................................................167
6.7.1 Euler Equation Model.............................................................................................................169
6.7.2 Sales Accelerator Model.........................................................................................................170
6.7.3 Error Correction Model (ECM)..............................................................................................171
6.7.4 Generalized Moments Method Estimator ...............................................................................172
6.8 EMPIRICAL RESULTS.......................................................................................................................174
CONCLUSIONS.....................................................................................................................................181
I. APPENDICES......................................................................................................................................... I
APPENDIX 1 – INEQUALITY DEMONSTRATION ........................................................................................I
APPENDIX 2 – DETAILED CLEANTECH TAXONOMY ...............................................................................II
II. BIBLIOGRAPHY .............................................................................................................................. IX
III. ANNEX......................................................................................................................................XXVIII
LIST OF ITALIAN CLEANTECH INNOVATIVE START-UPS....................................................................XXVIII

IV
List of Figures
FIGURE 1. FIRM VALUE AS A FUNCTION OF LEVERAGE RATIO 9
FIGURE 2. FIRM VALUE AS A FUNCTION OF LEVERAGE IN PRESENCE OF BANKRUPTCY COSTS 25
FIGURE 3. THE MYERS AND MAJLUF MODEL 32
FIGURE 4. PRIVATE BENEFITS EXTRACTION IN JENSEN AND MECKLING (1976) 38
FIGURE 5.UNCONSTRAINED FIRMS 44
FIGURE 6. FINANCIALLY CONSTRAINED FIRMS 45
FIGURE 7. FINANCING SOURCES AND LIFE CYCLE OF THE FIRM 58
FIGURE 8. TOTAL NUMBER OF BUSINESS ANGELS NETWORKS/GROUPS IN SELECTED COUNTRIES (2010) 68
FIGURE 9. VISIBLE INVESTMENT BY BUSINESS ANGELS IN SELECTED COUNTRIES (2009) 69
FIGURE 10. DISTRIBUTION OF BUSINESS ANGEL INVESTMENTS BY SECTOR IN SELECTED COUNTRIES 69
FIGURE 11. GLOBAL VENTURE CAPITAL INVESTMENTS 74
FIGURE 12. MEGA-INVESTMENTS BY REGION AND NUMBER OF ROUNDS (2014) 75
FIGURE 13. VENTURE CAPITAL INVESTMENTS BY INDUSTRY: NUMBER OF ROUNDS 76
FIGURE 14. VENTURE CAPITAL INVESTMENTS BY INDUSTRY: AMOUNT INVESTED (US$ BN) 76
FIGURE 15. MEGA INVESTMENTS BY SECTORS AND NUMBER OF ROUNDS (2014) 77
FIGURE 16. AMOUNT RAISED AND NUMBER OF EXITS THROUGH IPO EXITS BY REGION (2014) 78
FIGURE 17. AMOUNT RAISED AND NUMBER OF EXITS THROUGH IPO EXITS BY SECTOR (2014) 78
FIGURE 18. AMOUNT RAISED AND NUMBER OF EXITS THROUGH M&A EXITS BY REGION (2014) 79
FIGURE 19. AMOUNT RAISED AND NUMBER OF EXITS THROUGH M&A EXITS BY SECTOR (2014) 79
FIGURE 20. 2004-2014 YEARLY DISTRIBUTION OF VC DEALS 80
FIGURE 21. ITALIAN PRIVATE EQUITY MARKET COMPOSITION (2014) 80
FIGURE 22. 2014 VS 2013 DISTRIBUTION OF SHARES OF TARGET COMPANIES OWNED BY VC FIRMS 81
FIGURE 23. DISTRIBUTION OF VC INVESTMENTS BY DEAL ORIGINATION 82
FIGURE 24. VC INVESTMENTS BY REGION IN 2014: NUMBER OF DEALS 83
FIGURE 25. VC INVESTMENTS BY INDUSTRY AS A PERCENTAGE OF TOTAL VC INVESTMENT 84
FIGURE 26. CROWDFUNDING MODELS ADOPTED BY CFPS IN ITALY 90
FIGURE 27. CARBON DIOXIDE EMISSIONS BY SELECTED COUNTRIES 118
FIGURE 28. CLEAN TECHNOLOGY LIFE CYCLE 120
FIGURE 29. FOCUS OF VC INVESTMENTS 121
FIGURE 30. CLEANTECH SUBSECTORS CLASSIFICATION 122
FIGURE 31. CLEANTECH PUBLIC PURE-PLAY COMPANIES 126
FIGURE 32. CLEANTECH PUBLIC PURE-PLAY COMPANIES BY INDUSTRY (2012) 128
FIGURE 33. GROWING DISCONNECTION BETWEEN ITALIAN GDP AND DEMAND FOR POWER 129
FIGURE 34. ITALIAN ELECTRICITY PRICES 130
FIGURE 35. CUMULATIVE TREND REGARDING THE REGISTRATION OF INNOVATIVE START-UPS 140
FIGURE 36. MONTHLY START-UPS REGISTRATIONS TO SPECIAL SECTION OF THE COMPANIES REGISTER 140
FIGURE 37. DISTRIBUTION OF INNOVATIVE START-UPS REGISTRATION TO THE COMPANIES REGISTER 141

V
FIGURE 38. DISTRIBUTION OF INNOVATIVE START-UPS ACCORDING TO THEIR AGE 142
FIGURE 39. DISTRIBUTION OF INNOVATIVE START-UPS REGISTRATION TO THE COMPANIES REGISTER 143
FIGURE 40. COMPARISON OF CLEANTECH AND NON-CLEANTECH START-UPS ACCORDING TO THEIR AGE 143
FIGURE 41. REGIONAL DISTRIBUTION OF INNOVATIVE START-UPS ACCORDING TO THEIR AGE 146
FIGURE 42. DISTRIBUTION OF INNOVATIVE START-UPS PER REGION AS OF THE 16TH OF MARCH 2015 147
FIGURE 43. DISTRIBUTION OF INNOVATIVE START-UPS PER PROVINCE, AS OF MARCH 2014 148
FIGURE 44. DISTRIBUTION OF CLEANTECH START-UPS PER MACRO-AREA 151
FIGURE 45. SECTORAL DISTRIBUTION OF THE TOTAL SAMPLE 152
FIGURE 46. SECTORAL DISTRIBUTION OF THE CLEANTECH SAMPLE 153
FIGURE 47. COMPARISON OF THE NUMBER OF PROFITABLE COMPANIES 156
FIGURE 48. EFFECT OF ACCESS TO GUARANTEE FUND ON CAPITAL SUPPLY CURVE 161
FIGURE 49. EFFECT OF ACCESS TO GUARANTEE FUND ON CAPITAL SUPPLY CURVE 162

VI
List of Tables
TABLE 1. RETURNS IN THE POSSIBLE STATES OF NATURE 13
TABLE 2. EQUITY INVESTORS AT SEED, EARLY AND LATER STAGE OF FIRM GROWTH 65
TABLE 3. INFORMAL INVESTORS CLASSIFICATION 66
TABLE 4. ESTIMATES OF THE ANGEL MARKET AND COMPARISONS WITH VENTURE CAPITAL 67
TABLE 5. PROFILE OF VC AVERAGE INVESTMENTS IN ITALY (2014) 84
TABLE 6. PROJECT RECEIVED AND LAUNCHED BY CFPS IN ITALY 91
TABLE 7. ITALIAN INNOVATIVE START-UPS' LEGAL FORMS 144
TABLE 8. NUMBER OF START-UPS BY REGION AS OF THE 16TH OF MARCH 2015 145
TABLE 9. NUMBER OF START-UPS PER 1 MLN INHABITANTS AS OF THE 16TH OF MARCH 2015 149
TABLE 10. BOOK VALUE AND CAPITAL EMPLOYED, DESCRIPTIVE STATISTICS 155
TABLE 11. SALES AND NET PROFIT DESCRIPTIVE STATISTICS 156
TABLE 12. DEBT DESCRIPTIVE STATISTICS 157
TABLE 13. INVESTMENT, DESCRIPTIVE STATISTICS 158
TABLE 14. PROFITABILITY INDEXES, DESCRIPTIVE STATISTICS 158
TABLE 15. DEPENDENT VARIABLES 164
TABLE 16. INDEPENDENT VARIABLES 164
TABLE 17. CORRELATION MATRIX 165
TABLE 18. DESCRIPTIVE STATISTICS ON REGRESSION VARIABLES – NON-WINSORISED 167
TABLE 19. DESCRIPTIVE STATISTICS ON REGRESSION VARIABLES – WINSORISED 167
TABLE 20. EMPIRICAL RESULTS - BASIC VERSION OF THE MODELS 178
TABLE 21. EMPIRICAL RESULTS - AUGMENTED VERSION OF THE MODELS 179

VIII
Abstract
This work analyses the effect of the introduction of a public guarantee fund for small and
medium enterprises on start-ups’ investment cash flow sensitivity (ICFS), considered a proxy of
firms’ financial constraints. More specifically, the principal aim of the study is to identify
whether the access to the guarantee fund generates a particular differential effect of
reduction of the ICFS for cleantech start-ups, compared to non-cleantech firms.
The empirical analysis is based on a sample of 2,428 Italian start-ups, of which 521 cleantech,
registered to the Special Section of the Italian Companies Register. We compare empirical
results obtained using three different econometric models – Euler equation, sales accelerator,
error correction – and system generalized method of moment (GMM) techniques that take
into account the endogeneity of the access to public guarantee fund.
First, we find that the access to the guarantee fund generates a reduction of firms’ financial
constraints. Arguably, firms that did not access the guarantee fund are characterized by a
larger ICFS than those that did. Second, our results point out that, before obtaining the access
to the guarantee fund, cleantech start-ups present on average a larger ICFS and are more
financially constrained than non-cleantech ones. Finally, we show that the effect of reduction
of financial constraints produced by the access to the guarantee fund is larger for cleantech
start-ups than for non-cleantech ones: indeed, granted non-cleantech firms result to have a
larger ICFS than cleantech ones. We interpret this result as suggesting that the receipt of public
subsidies by cleantech firms is perceived as a commitment of the government in supporting
the development of clean technologies, thus reducing the perception of the policy risk that
discourages external financers from investing in cleantech.
Our findings evidence that public interventions can have a specific differential effect for some
sectors and can be more powerful if specifically directed towards industries that present
higher policy risk and dependence from regulations, such as cleantech.

IX
Abstract in Italiano
Questo studio analizza gli effetti generati dall’introduzione di un fondo pubblico di garanzia
destinato alle piccole e medie imprese sulla sensitività degli investimenti ai flussi di cassa (ICFS)
delle start-up, considerata una misura dell’intensità dei vincoli finanziari a cui esse sono
soggette. Più precisamente l’obiettivo principale del lavoro di ricerca è quello di mostrare se
l’accesso al fondo di garanzia genera uno specifico effetto differenziale di riduzione della ICFS
per le start-up cleantech, rispetto alle altre imprese non cleantech.
L’analisi empirica è basata su un campione di 2.428 start-up italiane, di cui 521
cleantech,registrate nella sezione speciale del Registro delle Imprese dedicata alle start-up
innovative. I risultati sono ottenuti confrontando le stime di tre diversi modelli econometrici –
Euler equation, sales accelerator, error correction – ottenute utilizzando come metodo di
stima il metodo generalizzato dei momenti (GMM) per tener conto dell’endogeneità
dell’accesso al fondo di garanzia.
I risultati dell’analisi evidenziano innanzitutto che l’accesso al fondo di garanzia determina una
riduzione dell’intensità dei vincoli finanziari. Infatti le imprese che hanno avuto accesso al
fondo di garanzia presentano una ICFS minore rispetto alle altre. Dall’analisi emerge inoltre
che, prima di ottenere l’accesso al fondo di garanzia, le start-up cleantech presentano in media
una ICFS maggiore rispetto alle non-cleantech, risultando quindi maggiormente soggette a
vincoli finanziari. Infine, il nostro studio mostra che l’effetto di riduzione dei vincoli finanziari
generato dal fondo di garanzia è maggiore per le imprese cleantech, rispetto alle non
cleantech. Questo risultato può essere interpretato come un segnale del fatto che
l’ottenimento di un finanziamento pubblico da parte di una start-up cleantech viene
interpretato come una dimostrazione dell’impegno del Governo nel fornire un supporto allo
sviluppo delle tecnologie “clean”, riducendo così la percezione del rischio che l’andamento del
settore cleantech possa mostrare degli andamenti negativi a causa dell’influenza dell’
instabilità delle politiche governative a supporto del settore.

X
Executive Summary
Start-ups represent a fundamental driver for the economic development of a country,
providing a relevant positive contribution to technological innovation and employment. For
this reason large enterprises, scholars and institutions are devoting increasing attention on
these innovative small firms.
Start-ups are the emblematic expression of entrepreneurial propensity: in an economic
environment characterized by high level of uncertainty and rapid radical changes, their lean
and flexible structure represents a key success factor for new business development and plays
a fundamental role in the innovation process.
A large body of literature has analyzed start-ups’ financing, highlighting that one of the main
obstacles to the development of such firms is represented by their struggle in raising capital
from external financial sources. Indeed, many factors such as the lack of a solid track record,
the absence of collaterisable tangible assets, and the fact that the validity of the
entrepreneurial business idea has not yet proved its viability, determine the presence of high
informational asymmetries between start-ups and external investors and a consequent
relevant risk of opportunistic behaviour. As a result, the cost of capital often appears too high
for these small innovative firms, increasing the relevance of their financing constraints.
Therefore start-ups typically face significant obstacles in filling the financial gap between the
availability of internal capital and the investment required to support the development and
the expansion of their business.
A fundamental aid to start-ups growth is provided by public interventions, including several
types of subsidies or financial incentives, both aimed at directly fund their investments and
indirectly improve their ability to attract external investors. Hence, the analysis of the
effectiveness of these public measures can provide crucial information to program new
efficient and effective strategies for the development and expansion of the economy of a
country.
In the last decades, global trends such as the increasing concerns on energy security issues, the
focus on the threats related to climate change, the perception of the scarceness of natural
resources and the development of a shared environmental consciousness are driving the
expansion of the clean technology industry. This sector, also known as “Cleantech”,
encompasses a variety of economical activities, that share some key common features: the

XI
focus on environmental sustainability, the development and utilization of innovative
technologies, and the presence of an underlying economical rationale with the aim of ensuring
financial sustainability.
Many recent studies have contributed to elaborate an exhaustive definition of the cleantech
sector, considered as one of the major potential leaders of the future economic expansion,
due to the increasing focus of developed and developing countries on environmental issues
and sustainable growth strategies, attested by the launch and renewal of international long-
term programs such as the Kyoto Protocol or Europe 2020. Both scholars and Governments
have focused on identifying the drivers of cleantech industry growth and consequently the
determinants of cleantech investing, because introducing policies to support the initial
development of this sector may be fundamental for the national economy in order to achieve
a sustainable strategic competitive advantage at a global level.
A noteworthy result of such work evidences the difficulty of clean technologies to attract VC
investment, base on the fact that this investment category results riskier than other VC
investments. The main reason of this higher risk level is identified in the fact that, compared to
the majority of other innovative and newly established industries, cleantech faces a higher
policy risk, for its development is strongly influenced by the presence of credible and
consistent government supportive policies and environmental regulations (Buer and
Wustenhagen, 2009; Ghosh and Nanda, 2010; Tierney, 2011; Cumming, 2013). Scholars
provided other arguments backing the hypothesis that cleantech sector faces higher difficulties
when seeking external funding, such as the absence of an established exit mechanism for VC
investment in cleantech (Ghosh and Nanda, 2010). However, such hypotheses are not
sustained by solid empirical evidence.
Given these considerations, and the relevant contribution of the academic research to the
definition of the strategic decision process adopted by policy makers, we consider of primary
importance the elaboration of an analysis on such a current topic as the development of
cleantech start-ups. More specifically, due to the fact that many authors have identified
financial obstacles as the principal barrier hindering start-ups growth, we focus our study on
the assessment of the effectiveness of public interventions on cleantech start-ups’ financial
constraints. To our knowledge our research is the first work focusing on the factors influencing
cleantech start-ups access to external capital.
Firms’ financial constraints have been the subject of analysis of a consistent strand of
literature. The problem moves from the work of Modigliani and Miller (1958) who introduce

XII
the importance of firms’ capital structure; from their work stem two separate streams, one
focusing on the trade-off between costs and benefits of debt, the other arguing that capital
market imperfections generate a distinct order of preference regarding the various forms of
funding. Both theories move from considering a ideal frictionless capital market, towards more
realistic models, where imperfections play a predominant role in determining firms’ choices. In
this process, the existence of financing constraints for companies, especially for those younger
and smaller, result more and more apparent and become a fundamental topic of current
financial research.
From this framework, moves the theory of investment-cash flow sensitivity (ICFS), first
proposed by Fazzari et al. (1988). Based on the assumption (resulting from the pecking order
theory) that the cost of external capital is an increasing function of the amount of capital
offered, such study demonstrated that financially constrained firms are characterized by a
larger ICFS, with respect to unconstrained ones. Hence, the authors argue that the level of ICFS
of a firm can be used as a proxy to assess the relevance of its financial constraints. Despite
having been subject to various objections, the aforementioned work by Fazzari et al. (1988)
has proven effective in a consistent number of studies regarding start-ups. In particular,
Colombo et al. (2013) argue the validity of the ICFS theory for new-technology-based firms, as
a consequence of qualities that are featured also by the cleantech start-ups analysed in our
work. Based on these results, in our study we attempt to measure the ICFS of the cleantech
start-ups, and view a positive ICFS as a sign of the presence of financial constraints that
determine an obstacle to their investment activity.
With respect to the public policies, governmental entities have been introducing subsidies in
order to reduce the financial constraint of firms, but whether public subsidies are beneficial to
innovative start-ups is still under question (Holtz-Eakin, 2000).
For these reasons, we considered interesting to focus the aim of our study on the empirical
assessment of the effects generated by the introduction of public supportive interventions on
the ICFS of firms at start-up stage. Moreover, we examine whether such public intervention
has a specific impact on cleantech start-ups’ ICFS, compared with non-cleantech firms.
A similar approach has been adopted by a number of previous studies which analysed firms’
ICFS in order to assess whether elements such as the introduction of public subsidies or the
receipt of venture capital financing generate a reduction of financing constraints (Bertoni et
al., 2010; Colombo et al., 2013). The results of these studies evidenced that the impact of
these factors on firms’ ICFS is particularly relevant for small innovative firms. The conclusions

XIII
are consistent with a substantial stream of literature on small firms financing, that argues that
start-ups typically face higher difficulties than larger mature firms when seeking for external
financing (Carpenter and Petersen, 2002; Hall, 2002).
The empirical analysis is based on a sample of data regarding 2,428 firms, selected from a total
population of 3,512 Italian start-ups registered to the Special Section of the Companies
Register dedicated to innovative start-ups. Among these firms, a group of 521 cleantech has
been identified adopting a structured methodology, based on the definition of a complete
taxonomy of the cleantech activities. We regard the systematisation of the cleantech sector as
one of the major contributions of the present work. The dataset has been completed including
financial statement figures for the selected companies. At the time of data collection (16th
of
March 2015) a total number of 5,139 financial statements were available, 1,248 of which
pertained to cleantech firms and 3,891 to non cleantech firms. To our knowledge, the resulting
list is the most exhaustive sample of Italian cleantech start-ups.
With Ministerial Decree 29th
April 2013, the Ministry of Economic Development proceeded to
dictate some criteria and simplified procedures granting free access to innovative start-ups
and certified incubators to the Guarantee Fund for Small and Medium-sized Enterprises. In this
study, we empirically investigate whether this public measure contributes to relax financial
constraints of Italian start-up firms. More precisely, our principal target is to assess if this
public guarantee fund generates a specific differential effects for the Italian cleantech start-
ups, with respect to the other Italian start-ups.
Following the approach adopted by previous works (Guariglia, 2008; Bertoni et al., 2010;
Bertoni et al., 2012; Colombo et al., 2013), we shall build three different econometric models –
Euler equation, Sales accelerator and Error correction model – to assess firms’ ICFS.
Introducing the dummy variables (that discriminates cleantech and non-cleantech
firms) and (that differentiate whether a firm obtained access to the guarantee fund or
not) we specify two different versions of each model: a “basic” version that allows assessing
the marginal effect of the access to the guarantee fund on firms’ ICFS, and an “augmented”
version that shall be used to investigate if there is a differential effect of the access to the
guarantee fund for cleantech start-ups, with respect to the non-cleantech ones.
Our results can be synthesised as follows.

XIV
First, we conclude that the access to the guarantee fund generates a reduction of firms’
financial constraints. Second, evidence points out that, before obtaining the access to the
guarantee fund, cleantech start-ups present on average a larger ICFS and are more financially
constrained than non-cleantech ones. Finally, we show that the effect of reduction of financial
constraints produced by the access to the guarantee fund is larger for cleantech start-ups. We
interpret this result as suggesting that the receipt of public subsidies by cleantech firms is
perceived as a commitment of the government in supporting the development of clean
technologies, thus reducing the perception of the policy risk that discourages external
financers from investing in cleantech.
Our innovative contribute is represented by the evidence that public interventions can have a
specific differential effect for some sectors and can be more effective if specifically directed
towards industries that present higher policy risk and dependence from regulations, such as
cleantech. From this point of view our estimates can be useful for policy makers both to assess
the effectiveness of the public guarantee fund in supporting Italian start-ups development and
to plan specific public interventions to sustain the growth of cleantech sector in Italy.
The work will be structured as follows:
The first section of the study is devoted to presenting a literature review of the topics related
to our study.
Chapter 1 will present the financing theories leading to the ICFS approach, and constituting the
theoretical background of our examination. Starting from the introduction of the importance
of capital structure by Modigliani and Miller (1958), we present the two main branches that
derived: trade-off theory and pecking order theory. From the latter stems the work of Fazzari
(1988) giving way to the ICFS theory that constitutes the foundation of our model.
Chapter 2 is devoted to the presentation of the studies regarding the determinants of start-
ups’ financing and their capital structure. In particular we will analyse human capital,
exogenous factors such as location and the presence of incubators, and the interconnections
with angel investors and venture capital, and the introduction of crowdfunding.
Chapter 3 contains an overview of the Italian legislation of innovative start-ups, specifically
introduced with the Legislative Decree N. 179/2012.

XV
Chapter 4. analyses the peculiarities of the cleantech sector, the main drivers of its emergence,
together with the factors influencing investment flows to the sector.
The second section will illustrate our empirical analysis.
Chapter 5. regards the data sample collection, in particular exposing the sources used to
collect the data and the methodology applied to identify cleantech firms. Finally we will
present a descriptive analysis of the samples, highlighting temporal trends, legal, geographical
and sectoral characteristics of the firms. Last, we will present some descriptive statistics
regarding financial figures of the firms under analysis. We will structure our examination
underlining the peculiarities of the cleantech sample.
Chapter 6 details the empirical process: we will start presenting the aim of the study, and the
assumptions underlying the model. We will proceed by describing the variables used in the
regressions, and presenting the econometric specifications of the models, and conclude
presenting our empirical results.
In the Conclusions, we analyse and interpret the results, discuss the weaknesses of our work,
and propose suggestions for further studies.

1
LITERATURE REVIEW AND
THEORETICAL BACKGROUND

3
1. Financing Theories
1.1 The Genesis of Capital Structure Theory
1.1.1 Modigliani-Miller Irrelevance Proposition
Many authors agree that the theory of business finance in a modern sense has its origins with
the Modigliani and Miller (1958) capital structure irrelevance proposition; even though there
were previous attempts (for example see Williams, 1938) to develop ideas over capital
structure choices, before the work of Modigliani and Miller there was no generally accepted
theory of capital structure.
In their seminal work, developed over a series of paper (Modigliani and Miller, 1958, 1961,
1963) Modigliani and Miller state that, under strict assumptions, a firm’s market value does
not depend on its capital structure, and therefore there is no cost differential between internal
and external finance.
The underlying assumptions of the model are (i) neutral taxes; (ii) symmetric access to credit
markets (i.e., firms and investors can borrow and lend money at the same interest rate); (iii)
absence of capital market frictions (i.e., no transaction costs, asset trade restrictions or
bankruptcy costs); and (iv) absence of information asymmetries. In their original paper (1958),
Modigliani and Miller also assumed that firms can be divided into "equivalent return" classes
such that the return on the shares issued by any firm in any given class is proportional to (and
hence perfectly correlated with) the return on the shares issued by any other firm in the same
class; nonetheless, Stiglitz (1969) showed that this assumption is not essential.
The proof of the so-called Proposition I is based on an arbitrage reasoning: the authors
imagine two companies with identical expected return, but different capital structures,
specifically one financed entirely with equity (from now on referred to as the unlevered firm)
and the other one resorting to debt financing (levered firm).
First of all, the authors analyse the case in which the value of the levered firm is larger than
that of the unlevered one: they proceed to demonstrate that, through personal borrowing, an
investor can create an arbitrage portfolio with a certain positive return without committing
any personal capital, buying shares of the unlevered firm, taking a precise amount of debt that

4
makes the leverage of the portfolio equal to that of the levered firm, and short selling shares
of the levered company. Since the return of the two companies is the same under the
abovementioned assumption of homogeneity, the yield of the shares of the unlevered
company, minus the interests paid on debt will match exactly the costs for the short position
on the levered shares (which pay the same return, minus the interests retained by the levered
company for the service of its debt). Such portfolio grants a capital gain given by the greater
price of the levered shares compared to the unlevered ones, as stated at the beginning of the
demonstration.
This makes the unlevered company more attractive to investors who will buy shares of the
unlevered company and sell shares of the levered, thus creating a mechanism with the final
result of depressing the value of the levered company and raising the value of the unlevered
one, until equilibrium is reached: therefore, under the hypotheses, a different price for the
companies cannot be consistently registered on the market.
The same reductio ad absurdum proof is used to demonstrate the opposite case, namely that
the market value of the levered company is less than that of the unlevered company: in this
situation, the investor will lend money at the only market interest rate, to create the arbitrage
portfolio.
As an empirical proposition, the Modigliani-Miller irrelevance proposition is not easily tested:
given the fact that debt and firm value are both plausibly driven by endogenous factors such as
profits and growth opportunities, we cannot establish a structural test of the theory by
regressing market value on debt (Frank and Goyal, 2007).
For instance, Fama and French (1998) carry out an empirical analysis to verify how a firm’s
value is related to dividends and debt: they design a regression to measure how taxation of
dividends and debt affects firm value. On the basis of previous literature, such as the
Modigliani-Miller theorem, the expected result would be that value were negatively related to
dividends and positively related to debt (or non-related to debt in case of the original
Proposition I).In reality, the authors find the opposite, and infer that dividends and debt
convey information about profitability (expected net cash flows) that obscures any tax effects
of financing decisions.
Conversely, numerous fairly reliable empirical relations between a number of factors and
corporate leverage have been found (see for example Colombo and Grilli, 2007): though not

5
disproving Modigliani-Miller theory, this fact does make it seem an unlikely characterization of
real business financing mechanisms.
1.1.2 Modigliani-Miller with Taxation
The hypotheses underlying Modigliani and Miller theory are evidently non-realistic, and the
consequent interpretation of Proposition I soon became that, when relaxing the hypotheses,
capital structure is indeed a relevant factor in determining the market value of a company.
In particular, Modigliani and Miller themselves relax the neutrality axiom and include taxation
in their 1963 paper; they reach the conclusion that the value of a levered company is equal to
that of an identical unlevered company, plus the present value of tax savings generated by
debt, for debt is deductible from taxable profits.
Such theory therefore states the existence of an implicit incentive to use debt rather than
equity (Adair and Adaskou, 2015), and that the relative convenience of debt over equity grows
with the tax rate. The obvious though paradoxical conclusion is that companies should resort
exclusively to debt financing.
The conundrum finds a first explanation in the existence of bankruptcy costs (Stiglitz, 1969)
that implies the necessity for a company to balance between tax benefits generated by the
differential fiscal treatment of debt, and the increase in financial distress-related costs of an
excess of leverage. Hence, companies face a trade-off problem driving to an optimal debt level
when marginal benefits associated with tax rebate are equal to the marginal costs of financial
distress due to leverage (Adair and Adaskou, 2015). This solution to the irrelevance matter
brought up by Modigliani and Miller is the foundation of trade-off theories, and will be further
discussed in the following Chapter 1.2.
1.1.3 Miller’s Debt and Taxes Model
Miller (1977) proposed another approach to the capital structure problem, as he introduced
taxes on personal earnings in addition to corporate taxation. The idea behind the model is that
the objective function for an investor does not consist in maximizing the value of the company
(often calculated through the Dividend Discount Model, as the net present value of future
gross dividends), but rather his or her final net income: this brings about the necessity to

6
consider the impact of taxes on dividends for shareholders, and taxes on interests for
bondholders.
Miller shows that, under certain conditions, personal income taxes paid by the marginal
investor in corporate debt exactly offset the corporate tax-saving advantage.
The result is a materially different kind of capital structure irrelevance, associated with
multiple equilibria: in fact, Miller’s theory identifies an economy-wide leverage ratio, valid for
the economic system in its entirety, determined by the intersection of the aggregate curves of
demand and supply of capital. In his theory, no optimal capital structure is determined for a
single company, conversely since the equilibrium only determines aggregates, debt policy
should not matter for any single tax-paying firm (given the aforementioned condition that
personal income taxes perfectly balance tax savings). Accordingly every company will generate
a demand for capital that will factor in the aggregate demand for capital curve, and at the
same time every investor will generate a supply of capital (equity or debt depending on his or
her specific tax rates) that will contribute to the aggregate supply of capital curve.
This theory preserves the irrelevance statement for the single company, while imposing an
aggregate optimal leverage, thus allowing us to explain the dispersion of actual debt policies
without having to introduce non-value-maximizing managers. A similar capital structure
irrelevance is proposed in Auerbach and King (1983).
Despite the importance of the new perspective proposed by Miller’s model, it was not
considered a realistic representation of capital structure determination; for instance, Myers
(1984) writes:
“Although Miller’s ‘Debt and Taxes’ model was a major conceptual step forward,
I do not consider it an adequate description of how taxes affect optimum capital
structure or expected rates of return on debt and equity securities”.
1.1.4 Influence on Following Literature
With regard to firm capital structure, the Modigliani-Miller Theorem opened a literature on
the fundamental nature of debt versus equity, and stimulated the rise of numerous theories
devoted to disproving irrelevance both in theory and as an empirical matter: the most
frequently used elements against Proposition I include consideration of taxes, transaction

7
costs, bankruptcy costs, agency conflicts, adverse selection, lack of separability between
financing and operations, time-varying financial market opportunities, and investor clientele
effects (Frank and Goyal, 2007).
Harris and Raviv (1991) provide a survey of such theories; we will analyse in detail some of
them in the following sections (Chapters 1.2 and 1.3).
Like many others, Frank and Goyal (2007) argue that “while the Modigliani-Miller theorem
does not provide a realistic description of how firms finance their operations, it provides a
means of finding reasons why financing may matter”: this description provides a reasonable
interpretation of much of the theory of corporate finance that followed in Modigliani and
Miller’s steps. Accordingly, it influenced the early development of both the trade-off theory
and the pecking order theory.
Nonetheless, as the following sections show, current progress in capital structure theory is no
longer based on re-examining the list of assumptions that generate the Modigliani-Miller
theorem to find a previously unrelaxed assumption.

8
1.2 Trade-off Theory
The term trade-off theory is used to describe a number of related theories, in which the
decision maker establishes the leverage of the company evaluating costs and benefits of
alternative leverage plans. The basic idea is that an optimal solution can be identified so that
marginal costs and benefits of leverage are balanced (Frank and Goyal, 2007).
As discussed in the Chapter 1.1, the first version of trade-off theory was a consequence of the
debate over Modigliani-Miller irrelevance proposition: when income tax was added to the
original proposition (Modigliani and Miller, 1963), debt resulted beneficial in that it shields
earnings from taxes and creates no offsetting cost. As a result, the objective function (i.e., the
firm’s value) is linearly growing with debt, which leads to an optimal structure of 100% debt
financing.
Such extreme conclusion is not only inconsistent with empirical data, it also gives a partial
representation of the capital structure determination process.
The first candidate to offset the benefits of debt has been identified in bankruptcy risk and
related costs: on the matter, Kraus and Litzenberger (1973) propose the idea that optimal
leverage is the result of the trade-off between the tax benefits of debt and the deadweight
costs of bankruptcy.
After that, a burgeoning theoretical literature has been developed attempting to reconcile
Modigliani-Miller Proposition I and Miller's ‘Debt and Taxes’ model with the balancing theory
of optimal capital structure (Bradley et al., 1984). The general result of this work is that if there
are significant leverage-related costs such as the aforementioned bankruptcy costs, but also:
 Loss of non-debt tax shields (see e.g., Bradley et al., 1984)
 Adverse selection cost of debt: the mechanism of adverse selection is central for
pecking order theories, but it also plays a role in trade-off theories. Halov and
Heider (2011) examine the effect of adverse selection mechanism on debt
issuance, arguing that if investors are uninformed about firms’ risk, they will be
willing to grant credit only at higher interest rates, as they factor in the price the
possibility of the company being riskier than what it seems. This makes debt
issuance much more costly, rendering an active management of the capital
structure towards the optimum more expensive. Given the fact that it is

9
paramount to the Pecking Order Theory, adverse selection and its impact on debt
costs will be discussed in further detail in Chapter 1.3.2.
 Agency costs of debt (see for example Jensen and Meckling (1976); agency theory
will be further discussed in Chapter 1.3.3.
The firm’s optimal capital structure will then involve the trade-off between the tax advantage
of debt and various leverage-related costs. These were the first examples of trade-off theories.
The figure below represents the optimization problem to be faced to maximise the firm value
using leverage. The optimal leverage ratio coincides with the peak of the curve.
Figure 1. Firm value as a function of leverage ratio
According to the definition provided by Myers (1984), a firm in a static trade-off framework is
viewed as setting a target debt-to-value ratio and gradually moving towards such target, in the
same way that a firm adjusts dividends to move towards a target payout ratio.
Frank and Goyal (2007) present a thorough discussion over Myers’ definition, in particular
focused on the following aspects: first, the target leverage ratio is not directly observable, it
can only be imputed from evidence. Second, the tax code is more complex than that assumed
by the theory, and this makes the definition of the leverage target quite blurry; in fact,
depending on which aspects of the tax code are included in the analysis, different targets can
0.96
0.98
1
1.02
1.04
1.06
1.08
1.1
1.12
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
FirmValue
Leverage Ratio

10
be identified. Third, the nature of bankruptcy cost must be defined more precisely to
determine the effect they have on a company’s value. Fourth, transaction costs must be
considered when analysing how the firm moves towards the target leverage ratio, and in
particular, adjustment marginal costs can increase when the adjustment is larger or be
considered roughly constant.
The problematic assumption of costless adjustment pointed out by Frank and Goyal is the topic
of a significant literature as it undermines the trade-off theory and many corresponding
empirical tests.
Myers’ himself writes that “there is nothing in the usual static trade-off stories suggesting that
adjustment costs are a first-order concern-in fact, they are rarely mentioned. Invoking them
without modelling them is a cop-out.”
In the absence of adjustment costs, the trade-off theory predicts that firms continuously adjust
their capital structures to keep the value-maximizing leverage ratio. However, in the presence
of such costs, firms may not find it optimal to respond immediately to shocks that push them
away from their target ratio. If the costs of such adjustments outweigh the benefits, firms will
wait to recapitalize, resulting in extended excursions away from their targets (Myers, 1984;
Leary and Roberts, 2005).
Fischer et al. (1989) show that even a small cost of recapitalization can result in long periods of
inactivity. The consequence of this view is that leverage will be persistent, in the sense that
firms will not always respond to shocks that perturb their capital structure.
On such topic, Leary and Roberts (2005) confirm that financing behaviour is consistent with the
presence of adjustment costs: in fact, they find that firms respond to changes in their equity
value due to price shocks or equity issuances by adjusting their leverage over the two to four
years following the change. According to the authors, the presence of adjustment costs often
prevents this response from occurring immediately, resulting in shocks to leverage that have a
persistent effect. They reach the conclusion that this persistence is more likely a result of
optimizing behaviour in the presence of adjustment costs, as opposed to market timing or
indifference.
For the previously stated reasons, the definition of trade-off theory shall be divided into two
parts; in accordance with Frank and Goyal (2007) we define static trade-off theory and target-
adjustment behaviour as follows:

11
Definition 1: A firm is said to follow the static trade-off theory if the firm's
leverage is determined by a single period trade-off between the tax benefits
of debt and the deadweight costs of bankruptcy.
Definition 2: A firm is said to exhibit target adjustment behaviour if the firm
has a target level of leverage and if deviations from that target are gradually
removed over time.
1.2.1 Static Trade-off Theory
As anticipated in the previous section, static trade-off theory models companies in a cross-
sectional framework and analyses the optimization problem that a company has to face to
balance the benefits (in terms of tax shield) and the costs of debt in order to reach the optimal
leverage ratio that maximises the firm’s value.
Subsequently, Bradley et al. (1984) develop a single-period model that is generally considered
the standard presentation of the static trade-off theory; they combine the essence of the tax-
advantage-and-bankruptcy-cost trade-off models of the aforementioned Kraus and
Litzenberger (1973) (but also Scott (1976), Kim (1978) and Titman (1984)) the agency-costs of
debt arguments of Jensen and Meckling (1976) and Myers (1977), the potential loss of non-
debt tax shields in non-default states, and the differential personal tax rates between income
from stocks and bonds in Miller (1977).
Other examples of non-dynamic trade-off models that deserve mentioning are Brennan and
Schwartz (1978), Titman and Wessels (1988), Rajan and Zingales (1995). In the remainder of
this paragraph, we will take as example the model developed in Bradley et al. (1984) to
illustrate static trade-off theory.
The assumptions underlying the model are the following: investors are risk-neutral, and face
progressive tax rate on returns from bonds, while the firm faces a constant statutory marginal
tax rate (it should be noted that risk-neutrality induces the investor to invest in whichever
security offers the better expected after-tax deal, and to invest in either all-equity or all-debt
portfolios depending on their tax rates). Both corporate and personal taxes are based on end-
of-period wealth; consequently, debt payments are fully deductible in calculating the firm’s

12
end of period tax bill, and are fully taxable at the level of the individual bondholder. Dividends
and capital gains are taxed at a single constant rate.
There exist non-debt tax shields, such as accelerated depreciation (depreciation of assets that
can be determined by management in order to reduce earning of the period and therefore
taxes) and investment tax credits, that reduce the firm's end-of-period tax liability. Negative
tax bills (unused tax credits) are not transferrable either through time or across firms (for
example, via a leasing agreement or through a merge).
Finally, the firm will incur in various deadweight costs associated with financial distress should
it fail to meet in full the end-of-period payment promised to its bondholders.
This assumption allows for the existence of costs associated with risky debt, that are incurred
when the firm has difficulties meeting its end-of-period obligations to its debt holders. In the
agency costs framework of Jensen and Meckling (1976) and Myers (1977), these costs include
the expenses of renegotiating the firm's debt contracts and the opportunity-costs of non-
optimal production/investment decisions that arise when the firm is in financial distress. In the
bankruptcy cost framework of Kraus and Litzenberger (1973), Scott (1976), Kim (1978), and
Titman (1984), these costs represent the direct and indirect costs of bankruptcy.
We use the generic term "costs of financial distress" to indicate both bankruptcy and agency
costs of debt since they both become significant only when the firm is in financial distress. In
other papers, e.g. Myers (1984), costs of financial distress include the legal and administrative
costs of bankruptcy, as well as the subtler agency costs, moral hazard costs, monitoring costs
and contracting costs, which can diminish firm value, even when formal default is avoided.
In the model presented in Bradley et al (1984) the firm's end-of-period value before taxes and
debt payments, X, is a random variable, specifically a continuous random variable. If the firm
fails to meet the debt obligation to its bond-holders, Y, the costs associated with financial
distress will reduce the value of the firm by a constant fraction k of X and will be zero
otherwise.
Let:

13
Under the above assumptions of the model, the uncertain end-of-period pre-tax returns to the
firm's stockholders and bondholders can be written as follows:
Table 1. Returns in the possible states of nature
State Debt Equity Tax Loss
1.
2.
3.
4.
Note that for every scenario, the total sum of returns is X.
Note, also, that the full use of the non-debt tax shield is capable of shielding from taxes an
amount equal to .
State 1 and 2 are evidently default cases: in state 1, the value of the company is negative, so
there will be no return for either of the security-holders. In state 2, the value of the company is
positive, but insufficient to pay the service of debt: therefore a default takes place, the
stockholders receive nothing, is lost as a deadweight cost of default, and the remaining
, if positive, goes to the debt-holders.
If earnings are large enough for equity not to default, then there remains the question of
whether the earnings are low enough that the non-debt tax shield is sufficient to cover the tax
liability and allow the company to pay no tax. Thus, states 3 and 4 differ with respect to
taxation.
The equation in state 4. shows that if pre-tax earnings are large enough for the firm to fully
utilize the non-debt tax shield , then the gross end-of-period return to stockholders is
. Debt-holders receive B as promised, and taxes are paid on the
remaining, minus the quantity shielded by non-debt shield: , which can
obviously be rearranged as shown in the table above.
Conversely, status 3 shows that if the firm's pre-tax earnings are such that
, (meaning that income is not sufficiently high and non-debt tax shields are not fully utilized),

14
the firm will pay no tax and the assumption of non-transferrable negative tax bills implies that
the end-of-period return to stockholders is .
The dividing point is when , that can be easily rearranged as:
which indicates that the return of the company is at least equal to the value of
debt plus the maximum quantity of earnings shielded by the non-debt tax shield.
The end-of-period pre-tax return to bondholders in status 2 follows from the assumption that
the costs associated with financial distress reduce the value of the firm by a constant fraction k
of X, while the fact that bondholders have limited liability in the event that the firm's end-of-
period value X is negative immediately determines their return in status 1.
Remembering that X has been defined as a random variable, with density probability function
, and considering:
we can calculate the beginning-of-period market value of the firm’s debt ( ) by integrating
the stockholder after-tax returns across different states:
Where the operator indicates the expected value.
Note that the first integral in the parenthesis refers to state 2, when the debt-holders receive
, while the second integral refers to states 3 and 4, when the debt-holders receive
in both cases. In state 1, debt-holders receive nothing.

15
Similarly, we can calculate the market value of the firm’s stock at the beginning of the period,
as:
Where is the pre-tax rate of return from stocks.
Note that the first integral refers to state 3, where stockholders receive , while the
second integral refers to state 4, where stockholders receive . In cases 1
and 2 stockholders receive nothing.
The addition of the two equations yields the market value of the company, V:
The last equation shows that the value of the firm is equal to the present value of the sum of
three expected values (the integrals). The first integral represents the situation in which X is
positive but insufficient to meet the debt obligation contracted by the firm. Under such
condition, the payment to the firm's bondholders is X less the total costs of financial distress
. Consistently with the assumption of a wealth tax, the payment to the firm's bondholders,
net of costs of financial distress, is subject to the personal tax rate .
The second integral represents the states of nature in which the firm's end-of-period pre-tax
value, X, is greater than its debt obligation, B, but less than the maximum level of earnings that
would result in a zero end-of-period corporate tax bill ( ). In these states, the firm
has no corporate tax bill; however, the payments to bondholders and stockholders are subject
to the personal tax rates, and respectively.
Finally, the third integral defines the net cash flows to the company’s bondholders and
shareholders if earnings are sufficient to pay the interests owed to bondholders and to fully
use the non-debt tax shield (given that they generate a positive corporate tax liability).
The key passage of the model is the assumption that the level of debt B is decided through the
solution of an optimization problem, for which the objective function is the equation for
beginning-of-period value of the company that we just derived. This means that the optimal
level of B is determined by maximising V. This is all but obvious, given the agency theories and

16
discrepancies between ownership and control of a company: for instance, the extraction of
private benefits by the management make so that the company structure aims at maximising
managerial welfare rather than the value of the company. We will discuss agency theory and
shareholders-management conflicts in Chapter 1.3.2.
In static trade-off theories, and in particular in the model of Bradley et al., such inefficiencies
and agency conflicts are assumed non-existing, and the problem is solved mathematically in
order to reach a closed-form solution.
So, the capital structure puzzle is reduced to the following maximisation problem:
As mathematical programming theory states, the optimal value of an optimisation problem
can fall either in an interior point or on the boundaries of the search space of feasible
solutions.
Interior points are characterised by satisfying of the first-order condition .
In the described model, this results in:
Solving in B the equation leads to the determination of the optimal debt level .
The first addendum in the equation is a modelling of the tax benefit generated by one
additional unit of debt. Conversely, second and third addendum represent expected costs
generated by leverage: specifically, the second term represents how much one additional unit
of debt would increase the probability of wasting interest tax shields when gross income is
smaller than the amount covered by non-debt tax shield (this case refers to state 3 in Table 1).
The third term represents how much an additional unit of debt would increase the expected
cost of distress.
To extract useful predictions from the model it is sufficient to re-differentiate the first order
condition in the parameters of interest. With regard to this operation, we remember that cross
partial derivatives represent the effect that a positive variation of the second parameter of
differentiation has on the first-order derivative: given that the first-order derivative in our case
is let equal to zero (in order to determine the optimal debt quantity), we conclude that the

17
sign of the cross partial derivatives are directly related to the effect of the parameter on
optimal debt leverage.
Resulting cross partial derivatives are immediately calculable:
1.
The cross partial derivative is negative because all the factors are positive by definition, and
preceded by a minus sign. As a result, we can conclude that, in accordance with the theory, an
increase in the cost of financial distress (k) will reduce the optimal leverage.
2.
Again, every factor is positive by definition, but preceded by a minus sign. This implies that an
increase in non-debt tax shields ( ), and the consequent increase in the shielded amount of
earnings ( , leads to a lower optimal level of debt. The logical explanation is that non-
debt tax shields reduce the tax benefit provided by debt.
3.
To demonstrate the inequality above, we note that, since is a monotonically increasing
function, the following chain applies:
This demonstrates that an increase in the personal tax rate on stock ( ) increases optimal
debt level.
4.
Demonstration of such result is more laborious than the previous ones; it will be presented in
Appendix 1.

18
The result shows that the effect of an increase in the personal tax rate for a bondholder is to
decrease the optimal leverage. Note that this is true only at the optimal capital structure, while
it is not always true if the company is far from the optimal structure.
Lastly,
5. Defining risk as the volatility of a company’s result ( ), Bradley et al. (1984)
demonstrate that if the other parameters assume “reasonable” values, the effect of volatility is
to reduce optimal leverage. The risk factor will be further discussed in our analysis, in
particular in Chapter 2 as one of the peculiarities of start-ups and especially cleantech start-
ups is the high risk to which they are subject.
The model presented nests numerous theories as particular cases. For example, in the specific
case analysed in Miller’s (1977) irrelevancy model, there are no tax on income from stocks, and
no leverage-related costs, which means . In such case, the derivative above is
reduced to:
The first addendum is the marginal expected value of the tax benefit from debt, while the
second addendum is the marginal tax premium that the firm expects to pay to its bondholders.
Notice that everything is expressed as a statistical equation, as the end of period value of the
firm is a random value, and therefore returns for security-holders are non deterministic.
To better define the first addendum, consider that when debt is risky (and so there is a
concrete possibility that it will not be repaid), the marginal expected value of the tax shield
generated by one unit of such debt is equal to the corporate tax rate multiplied by the
probability that the firm will repay its debt, given by .
From the first-order condition obtained in this specific case, authors conclude that:
“firms will issue debt up to the point where the marginal tax premium they
expect to pay to bondholders, , is equal to the marginal

19
expected tax benefit of debt, . Firms in the economy will
continue to issue debt until, due to the progressivity of the personal tax
schedule, equals . Thus, in equilibrium the net tax advantage of debt is
zero” (Bradley et al., 1984).
The main weakness of static trade-off models like the one we have presented, is that they
operate in a cross-sectional framework, while real companies operate over many periods.
This generates two further criticalities:
i) by construction, the model does not take into account retained earnings, that would instead
represent internal equity automatically created (if the company is profitable), with different
costs and benefits than other forms of capital: this would generate a strong impact on the
objective function and on the resulting optimal capital structure.
ii) cross-sectional models do not take into any account the dynamics of the model, meaning
that they determine an optimal equilibrium but fail to describe how companies move towards
the solution, how much time they take, and how they behave when they are far from the
optimal solution. More specifically, there is no proof of the actually mean-reversion of the
variable, which means that if a company finds itself in a suboptimal position, the model does
not provide evidence that the company will move towards the optimum.
Such critical aspects have generated a considerable dissatisfaction with the static trade-off
theory (Frank and Goyal, 2007); many authors abandoned the whole concept of trade-off
between costs and benefits of debt, and gave birth to an alternative line of research which
dominated corporate finance for decades (see for example Jensen and Meckling (1976), Myers
(1984), Myers and Majluf (1984); they will be the object of Chapter 1.3).
However, in recent years, taxation and bankruptcy costs of debt have made a comeback in
financial literature, this time featured in models where firms are analysed for more than one
period, in a time-series framework, originating the “dynamic trade-off theory”.

20
1.2.2 Dynamic Trade-off Theory
Dynamic trade-off models depart materially from static trade-off models. Analysing firms in a
way that recognises the role of time entails considering aspects neglected by cross-section
models, such as future expectations of the companies, and the adjustment costs generated by
modifying the capital structure.
Future expectations refer to the fact that a company will have to consider its future needs of
capital, and its future cash-flow generation, in order to decide if there is an advantage in
modifying the capital structure: in fact, to pay out today and having to raise funds tomorrow
can be strongly suboptimal because of taxes and costs of raising capital.
Not only, a company will also have to consider the rates of return that it generates and
confront them to the returns that its shareholders can get from the market. In this regard, it is
easy to demonstrate that if the company generates extra-profitability compared to the market,
it will create value by retaining more earnings, while conversely, if it is less profitable than
market returns, its value will grow by increasing the payout ratio (see, for example, Azzone et
al., 2005).
To do so requires a company to take into account its expectations about future profitability
and market trends, when determining its present capital structure.
Moreover, in contrast with static trade-off theories, dynamic trade-off theories take into
account the fact that it is costly to issue or repurchase debt. Thus, firms will modify their
capital structure to adjust to their optimal capital structure only when the benefits are greater
than the costs of adjustment.
Modern dynamic trade-off theories have a forerunner in Stiglitz (1973), who did not develop a
trade-off theory, but rather a multi-period model to investigate the effect of taxation on
corporate financial structure. Presenting the model, Stiglitz writes: “what the earlier studies
[...] lacked was a complete analysis of the interrelations [...] which become apparent in a multi-
period model“ (Stiglitz, 1973, page 6).
The result is a first example of dynamic analysis of the effect of taxes on the capital structure.
However, it is not a Trade-off theory, as Stiglitz concludes that for reasonable values of the
parameters there is a clear financial hierarchy to be followed when seeking funds and that
consequently “the actual debt equity ratio is the fortuitous outcome of the profit and
investment history of the firm” (Stiglitz, 1973, page 32).

21
The introduction of dynamic trade-off theories in a strict sense is due to the models developed
by Kane et al. (1984) and Brennan and Schwartz (1984) who investigate the trade-off between
tax savings and bankruptcy costs of debt in a multi-period framework. The models incorporate
taxes, bankruptcy costs, investment policy, uncertainty, and develop an optimal control
problem where firms determine at the same time the level of debt they need and the amount
of capital they intend to invest.
Both models work with continuous time, but do not consider transaction costs: this implies
that firms react immediately to shocks that determine a change in their capital structure, by
rebalancing the leverage to its optimal value, without having to face any cost. Since a negative
event can be immediately faced, according to these models, firms find convenient to maintain
a high leverage, for they can adjust as soon as debt grows and the threat of bankruptcy
becomes a possibility. This means that, assumed away transaction costs, firms could carry
large amounts of debt and, by the appropriate repurchase strategy, capture large tax shields
while keeping the debt essentially riskless (Fischer et al., 1989). In reality, though, firms display
a much lower leverage level than that predicted by the model, so the empirical testing of the
models did not meet large success.
Mello and Parsons (1992) develop a model based on Brennan and Schwartz (1985) example of
a firm that owns a mine with a commodity inventory that can be extracted: the mine can be
either open and working, closed but paying maintenance, or costlessly abandoned. The model
is designed to reflect the incentive effects of the capital structure and thus measures indirectly
the agency cost of debt. Moreover, the authors use the model to compare agency costs
associated with different maturity lengths of similar debt instruments.
Fischer et al.(1989) face the aforementioned problem of immediate and costless rebalancing
by including recapitalisation costs into the previous capital structure models. They hence
develop a dynamic optimal capital structure model built upon the traditional tax and
bankruptcy cost trade-off theory of capital relevance, in a continuous-time framework.
According to the authors, static models suffer the limitation that they neglect the optimal
restructuring choices that firms should undertake when asset values fluctuations move the
capital structure away from its optimal level. Transaction costs allow for the level of debt to
drift without the company adjusting in any way, as the costs of adjusting would be greater
than the benefits of the better structure, while only when the leverage gets too far out of line,
the firm rebalances its debt level: firms wait until the increased tax benefits outweigh the debt
issuance costs before increasing their leverage

22
When a company retains profits, its leverage level will decrease, while when it suffers losses
debt increases; this generates a drift in the capital structure of companies that is registered by
many empirical studies. However, a recapitalisation of debt or equity will take place only when
the level of the leverage will go below the lower or upper limit, respectively.
Fischer et al. numerically solve the optimisation model, and reach the conclusion that small
transaction costs are sufficient to allow for companies to undergo material drifts from the
optimal leverage. Besides, the model also provides distinct predictions and outlines a
relationship between firm-specific properties and the range of optimal leverage ratios:
“smaller, riskier, lower-tax, lower-bankruptcy cost firms will exhibit wider swings in their debt
ratios over time” (Fischer et al., 1989, page 39).
Leary and Roberts (2005) empirically examine capital structure rebalancing behaviour, in the
presence of costly adjustments and conclude that the dynamic trade-off model by Fischer et al.
is consistent with their empirical evidence; moreover, they show that the model is capable of
representing many aspects of the dynamics of firms’ leverage.
Other results of the model include:
1. Corporate tax rate increases debt tax benefits, while personal tax rate decreases them.
2. An increase in volatility determines a greater range over which the company allows leverage
to drift, and a reduction in the target debt level to which the firm recapitalizes when limits are
crossed. Therefore, volatility is negatively related to debt level, even if in a complex way.
A controversial aspect of the model presented in Fischer et al., (1989) is that if a company is
consistently profitable, its earnings will diminish debt to the point that a debt issuance will be
necessary: this generates the paradoxical conclusion that good performance is eventually
followed by a debt issuance.
Frank and Goyal (2007) provide a helpful taxonomy of dynamic trade-off papers, divided
according with the treatment of investment: many models such as Kane et al. (1984), Fischer
et al. (1989), Goldstein et al. (2001) consider cash flow as an exogenous variable, meaning that
they are determined by outside market conditions and not by the capital structure choices
under investigation.
Conversely, the way a company finances its investments can have an impact on investment
results and consequently on a company’s cash flows. Therefore, a number of papers have been
written considering investments as an endogenous variable of the model, influencing and

23
being influenced by the capital structure decision. In this number we include the previously
mentioned Brennan and Schwartz (1984), Mello and Parsons (1992), Titman and Tsyplakov
(2007), Hennessy and Whited (2005).
As previously stated, retained earnings are an important aspect for dynamic trade-off models.
In trade-off theory, earnings are generally modelled through a stochastic variable, while excess
cash flow generated is often assumed to be paid out to shareholder. However, the payout ratio
varies sensibly in different papers. For example, Brennan and Schwartz (1984) and Titman and
Tsyplakov (2007) base their model on the assumption that companies pay out all excess cash
flow on the same year they are generated, thus assuming away retained earnings, with an
important loss of empirical predictivity.
Conversely, Hennessy and Whited (2005) deepen the analysis on the interaction between
investment decisions and leverage policy; developing a dynamic model with endogenous
choice of leverage they take into account the possibility for companies to retain earnings in
accordance with their future needs.
Dynamic trade-off literature also investigates the value of having the option to postpone
capital structure decisions to the following period.
Goldstein et al. (2001) propose a model of dynamic capital structure where a firm has the
option to increase debt level, as opposed to a case in which a firm is constrained to a static
capital structure decision. Based on the consideration of Gilson (1997) that transaction costs
discourage debt reductions outside Chapter 11 (the title of the U.S. Bankruptcy Code regarding
restructuring under the bankruptcy law), Goldstein et al (2001) neglect the option to
repurchase outstanding debt. The immediate consequence of the possibility to increase debt
in the future is that management will initially issue a smaller amount of debt. Furthermore,
bonds issued by a firm with the possibility to increase its debt are riskier (because obviously a
future increase of debt would raise the probability of bankruptcy also for pre-existing bonds).
The study concludes that when a firm has the option to increase future debt levels, the tax
advantages of debt increase significantly compared to static-model predictions, and both the
optimal debt level range and predicted credit spreads paid on risky debt are more in line with
what is observed in the empirical evidence.
However, Goldstein et al. (2001) themselves admit that neglecting in the adopted framework
issues such as asset substitution, asymmetric information, equity’s ability to force concessions,
Chapter 11 protection, and many other important features does indeed affect optimal strategy
at a lower boundary.

24
The work by Hennessy and Whited (2005) investigates the relationship between investment
decisions and financing policy. The authors develop a dynamic trade-off model with
endogenous choice of leverage, capital distributions, and real investment in the presence of a
graduated corporate income tax, individual taxes on interest and corporate distributions,
financial distress costs, and equity flotation costs. Results show that firm capital structure is
indeterminate and depends on the firm’s financing deficits and their anticipated tax regimes.
The main results of dynamic trade-off theory – in terms of marginal effects on a company’s
value – are (Dudley, 2007):
i) they predict that the optimal debt level will grow with the corporate tax rate, as debt fiscal
benefits increase with the tax rate. Also, default risk associated with assets increases with the
tax rate, for higher taxes increases the likelihood of bankruptcy. Finally, the greater tax
benefits make a more frequent debt adjustment convenient, as the possible gains from a
better capital structure increase.
ii) the optimal leverage ratio increases with the interest rate and the same does the usefulness
of frequent debt adjustment and of narrow recapitalisation boundaries. This finds an
explanation in the fact that, even though higher interest rates do not affect the debt tax shield
(because higher discount rates compensate for higher coupons), they do reduce the present
value of bankruptcy costs. The resulting effect is positive, thus increasing the benefits to
adjustment when leverage is away from its target.
iii) volatility will push the company to widen the boundaries beyond which debt is adjusted, in
order to reduce the frequency of balancing and lower transaction costs.
iv) an increase in adjustment costs raises the cost of an active management of debt. To
decrease the number of adjustments, then, companies will widen recapitalisation boundaries;
moreover, this will decrease the optimal leverage, as an optimal leverage requires a greater
number of adjustments.
v) high bankruptcy costs will make debt riskier, thereby reducing the optimal level of debt.
Because there is less debt, the firm will default later. Higher bankruptcy costs reduce the upper
and lower bounds on leverage because the costs to increasing leverage are higher. This can be
seen in Figure 2. The first derivative of firm value against leverage in the growing part of the
function is smaller with high bankruptcy costs.

25
The figure below displays firm value as a function of leverage ratio for different bankruptcy
costs. The horizontal lines represent the recapitalisation boundaries: only when the firm goes
below its boundary, it will find convenient to adjust its leverage.
Figure 2. Firm value as a function of leverage in presence of bankruptcy costs
Adapted from Dudley 2007
A common property of dynamic trade-off models is that the optimal policy is invariant to firm
size (Dudley, 2007). However, if adjustment costs have a fixed component, independent from
the size of the adjustment – which is equal to saying that adjustment costs are proportionally
higher for smaller firms – then larger .firms should follow a different behaviour than small
firms. And in practice, adjustment costs do really have a fixed components connected to
issuance fees, rating fees, and so on that do not depend on the size of the emission.
Since tax-shield benefits of debt are the same for big and small firms, optimal capital structure
differences between such categories will depend only on readjustment costs. This means that
for small firms, fixed costs will make the costs of adjustment larger compared to its benefits.
As a result, small firms should have wider leverage boundaries inside which to drift freely
without adjusting the capital structure.
In other words, according to dynamic trade-off theories, small firms’ capital structures will
present a greater volatility around the optimal value predicted by the model.
Low bankrupcy
costs
High Bankrupcy
costs
0.7
0.8
0.9
1
1.1
1.2
0 0.2 0.4 0.6 0.8 1
FirmValue
Leverage Ratio
Boundary for low bankruptcy costs
Boundary for high bankruptcy costs

26
1.3 Pecking Order Theory
While in trade-off models there are two causes of inefficiency – the agency costs of financial
distress and the tax-deductibility of debt – that determine the optimal capital structure, Myers
(1984) and Myers and Majluf (1984) propose an alternative model based on market frictions
due to asymmetric information between internal managers, who act in the interests of the
owners, and outside investors.
The idea behind this alternative approach is that not all sources of funding are equal, and to
the contrary there is a precise order of preference that firms follow when raising capital.
In particular, the theory considers three different financing sources available to a company:
retained earnings, debt and equity.
The explanation of firms financing choices is based on the following ideas (Myers, 1984):
1. Firms prefer internal finance, which they obtain in the form of retained earnings
2. Firms adapt their dividend payout ratios to their investment opportunities, even though
dividends are notoriously sticky (meaning that firms try to maintain their level as stable as
possible, in good and bad times). Therefore, target payout ratios can only be adjusted
gradually in order to meet the requirements for valuable investment opportunities.
3. The aforementioned sticky dividend policies, added to unpredictable fluctuations in
profitability and the unexpected opening of investment opportunities, determine internally-
generated cash flow to be oftentimes greater or smaller than investment expense. If it is
smaller, the firm first resorts to its cash balance or marketable securities and, in case this were
not enough, it will have to chose between renouncing to the investment or raising capital.
If it is greater, then the firm enjoys excess cash flow that can be used to reduce debt, increase
cash or tradable securities and, in the long run, increase payout ratio.
4. If external financing is strictly necessary, firms will prefer to issue the safer security first. This
means that a company will issue debt first, then hybrid securities (i.e., securities that combine
characteristics of debt and equity, for example convertible bonds, capital notes), and only as a
last resort they will raise equity capital.
In this way of interpreting the capital structure puzzle, there is no optimal debt leverage, and
consequently there is no target capital structure. Instead, there are two kinds of equity:

1.3 Pecking Order Theory
27
internal equity, that is the preferred financing source for firms, and external equity that is the
least favourite financing source.
The resulting financing hierarchy is then the following
i) Internal equity, in the form of retained earnings
ii) Debt, in decreasing seniority order
iii) Hybrids
iv) External finance
In order to understand the reasons that lead to such conclusions, though, we need to take a
step back and analyse the theories that constitute the premises for the pecking order theory.
1.3.1 Theoretical Foundation of Pecking Order Theories
Knowledge of the existence of a pecking order hypothesis dates back at least to the 1960s; for
instance, Donaldson (1961) notes that management favours internally-generated capital as a
form of financing, to the point of excluding external sources except for “occasional
unavoidable bulges in the need for funds”(Donaldson, 1961). In order to meet this unexpected
surges of capital need, the “utilization of internally generated funds which cut into the
accustomed dividend per share would have a substantial cost in the adverse impact on market
price of the common stock”: such cost makes it prohibitive to fund these “bulges” with internal
cash flow, thereby making it necessary to resort to alternative sources of finance. Again
Donaldson notices an abnormally strong reluctance to sell common stock, that is even more
surprising considering the very high Price-Earnings ratios registered on the markets in those
years (in fact, high Price-Earnings are an indication of a high relative price of companies, which
makes it more convenient for previous owners of companies to raise equity capital).
Despite the general perception of the existence of such preference, a theoretical foundation of
the pecking order theory was still missing, until the paper by Myers (1984) built a logical
construction that justified the financing hierarchy so clearly observed in the market.
In particular, such paper constructs its explanation of pecking order theory resorting to other
previous works, interpreting the pecking order as a consequence of adverse selection that is
adduced as the reason for the reluctance of management towards source of financing other
than internal equity.

28
However, despite being often considered a consequence of adverse selection, pecking order
behaviour can also be generated by other economic forces (Leary and Roberts, 2005).
Such forces include agency costs (Myers, 2003), and taxes (Stiglitz, 1973, and Hennessy and
Whited, 2004). Baker et al. (2007) argue that, in the absence of other distortions, an excess in
managerial optimism can justify the presence of pecking order behaviour.
In the remainder of the chapters, we briefly present the financial forces underlying pecking
order theory, that give way to different kinds of pecking order theories. In particular we will
focus on adverse selection (Chapter 1.3.2) and agency theory (Chapter 1.3.3).
1.3.2 Adverse Selection
The most commonly adduced motivation for the pecking order observed in the market is the
adverse selection mechanism. This factor is the foundation for Myers (1984) and Myers and
Majluf (1984).
These papers exclude models that cut the bond between managers’ and stockholders’
interests, assuming identity between the two (Myers 1984). The founding idea is that the
owner-manager of the company knows the true value of the firm and the investments it is
undertaking. Conversely, external investors do not know them and can only form an idea
subject to error. This fact generates reluctance for the investor to pay the full price for a
company that might hide problems. Hence, a company in good conditions will try to avoid
external capital markets, as they are not willing to pay a good company’s price.
I. Akerlof ‘s Market for Lemons and Adverse Selection
In his famous market for lemons model, Akerlof (1970) describes an inefficiency of the market,
determined by an asymmetrical distribution of information between the buyer and the seller
in an economical transaction. He shows how market fails when buyers cannot verify the
quality of what they are offered: given the risk of buying a “lemon”, acquirers will be willing to
pay a lower price, which has the final consequence of impeding potential sellers of items in
good state from selling their product.
The argument is made through the analysis of a market of second-hand automobiles. For the
sake of clarity the author assumes that only four kinds of cars exist. There are new cars and

Financing Constraints and Investment-Cash Flow Sensitivity of Cleantech Start-ups in Italy - Dossi Girasoli

Financing Constraints and Investment-Cash Flow Sensitivity of Cleantech Start-ups in Italy - Dossi Girasoli

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