Conflicting Security Laws and The Democratization of Credit:
France’s Reform of the Napoleonic Code*
Kevin Aretz
Manchester Business School
kevin.aretz@mbs.ac.uk
Murillo Campello
Cornell University & NBER
campello@cornell.edu
Maria-Teresa Marchica
Manchester Business School
maria.marchica@mbs.ac.uk
This Draft: May 15, 2015
Abstract
We exploit contradictions in a recent security law reform in France (Ordonnance 2006-343 ) to study
how access to collateral shapes the composition of corporate debt, the distribution of debt across firms,
and capital allocation efficiency. Ordonnance 2006-343 modernized France’s 200-year old security law
system, in place since Napoléon Bonaparte, and it facilitated asset pledgeability in a country with a
highly unequal access to corporate debt. Despite this, the reform was undermined by “non-codified
laws,” introduced in the 1980s due to concerted lobbying by big firms wishing to pledge their liquid
assets (receivables and inventory). Using a triple-differences strategy, we show that firms with the
highest demand for fixed assets increased the use of debt financing the most after the reform (intensive
margin). Also, the fraction of “zero-leverage” firms among them dropped from 88% to 32% (extensive
margin). Using contract-level data, we further find that fixed assets allowed for significant reductions
in loan mark-ups and increases in debt maturity after the reform. These effects stem from the use of
secured debt and affect only private firms. Small, profitable, low risk-firms and start-ups benefitted
the most from the reform. Our geographical analysis shows that the reform benefitted firms in rural
areas, and that it led to a pronounced decline in the Gini-index of debt concentration across the whole
of France. Finally, we show that the reform increased capital allocation efficiency. All together, our
results suggest that the 2006 reform led to a “corporate debt democratization.”
Key words: Security Laws, Contractibility, Collateral, Capital Structure, Social Welfare.
JEL classification: G32, K22, O16.
*We are grateful to Marie-Elodie Ancel from Université Paris Est Créteil, Rod Cork from Allen & Overy (Paris),
and Philip Wood from Allen & Overy (London) for insightful discussions about the French legal system.
Conflicting Security Laws and The Democratization of Credit:
France’s Reform of the Napoleonic Code
Abstract
We exploit contradictions in a recent security law reform in France (Ordonnance 2006-343 ) to study
how access to collateral shapes the composition of corporate debt, the distribution of debt across firms,
and capital allocation efficiency. Ordonnance 2006-343 modernized France’s 200-year old security law
system, in place since Napoléon Bonaparte, and it facilitated asset pledgeability in a country with a
highly unequal access to corporate debt. Despite this, the reform was undermined by “non-codified
laws,” introduced in the 1980s due to concerted lobbying by big firms wishing to pledge their liquid
assets (receivables and inventory). Using a triple-differences strategy, we show that firms with the
highest demand for fixed assets increased the use of debt financing the most after the reform (intensive
margin). Also, the fraction of “zero-leverage” firms among them dropped from 88% to 32% (extensive
margin). Using contract-level data, we further find that fixed assets allowed for significant reductions
in loan mark-ups and increases in debt maturity after the reform. These effects stem from the use of
secured debt and affect only private firms. Small, profitable, low risk-firms and start-ups benefitted
the most from the reform. Our geographical analysis shows that the reform benefitted firms in rural
areas, and that it led to a pronounced decline in the Gini-index of debt concentration across the whole
of France. Finally, we show that the reform increased capital allocation efficiency. All together, our
results suggest that the 2006 reform led to a “corporate debt democratization.”
Key words: Security Laws, Contractibility, Collateral, Capital Structure, Social Welfare.
JEL classification: G32, K22, O16.
1
Introduction
Over the last years, many countries have reformed their security laws, a trend that is expected to
continue as more economies converge to the global capital market. Economic policy-makers promote these reforms under the banner of “enhancing access to credit,” and they view them as a
means to the ultimate goal of reducing discriminations in access to credit. Thus, the current
reforms are less about the ability of large, established firms to tap debt markets — and more
about the growth and value that can be unlocked by easing credit access across a larger number
of firms (“debt democratization”). In particular, the current reforms are targeted at profitable,
but financially-constrained firms, firms located outside of large urban areas, younger firms, and
firms in less established but growing sectors of the economy. Reducing discriminations in access
to corporate debt is a key factor since in most countries smaller companies employ more than
half of all employees and create more than half of all value-added (Infelise (2014)).
Despite the comprehensive goals of most security law reforms, recent academic work has
primarily focused on whether these reforms have raised total corporate debt (see Lilienfeld-Toal
et al. (2012), Vig (2013), and Campello and Larrain (2014)). A possible reason for this narrow
focus may be the limited availability of information on access to credit in those countries analyzed in other studies: namely developing countries. In this paper, we circumvent this problem
by studying a security law reform recently implemented by a developed country. This reform
is Ordonnance 2006-346, enacted in France in 2006. For France, we have data on important
dimensions related to credit access and economic outcomes that are unavailable for developing
countries. Another advantage of studying France is that, despite the country’s motto promoting
equality (“Liberté, Égalité, Fraternité”), access to credit in France was heavily concentrated in
the hands of a few large firms before the reform. For example, before the reform, 90% of the
private firms in our sample held no long-term debt and the average Gini index of long-term debt
concentration was around 0.95. Given these numbers, it is hardly surprising that an explicit
goal of the reform was to enable financially-constrained firms to borrow.1
Ordonnance 2006-346 derogated the “possessory” nature of asset ownership, in place since
Napoléon Bonaparte. In addition, it also allowed for the creation of security interests over types
1
Indeed, in association with Ordonnance 2006-343, the Bank of France declared that “reforms in this area
could have a significant effect on the distribution of credit, especially in case of borrowers that are currently
excluded [from credit markets].”
1
of assets which could hitherto not be pledged, as, for example, fungible or future assets, and
of security interests that can be charged to more than one creditor. Despite the fundamental
changes made by Ordonnance 2006-346, several “non-codified” security laws already existed in
France long before the reform. Of these non-codified laws, the two most popular were the Dailly
Law and the Pledge of Ready Money. The Dailly Law and the Pledge of Ready Money allowed
borrowers to pledge cash, cash equivalents, and receivables to financial institutions to secure
credit. Because of the existence and modern flavor of these two laws, we reason that Ordonnance
2006-346 had a more pronounced effect on those firms whose asset structures are tilted more
towards fixed rather than current assets. Thus, the coexistence of these “contradictory” laws
allows us to define an identification strategy in which the treated firms are those heavily using
fixed assets, and the controls are those heavily using current assets.
Our empirical tests analyze both firm-level (quantity) and contract-level (price) information
within a triple-differences framework. As a starting point, our tests show that the 2006 collateral
reform profoundly changed the magnitude and composition of the corporate debt held by
private French firms. Following the reform, the mean total debt-to-assets ratio increases from
a pre-reform average of 9.1% to a post-reform average of 12.3%. Notably, this increase is only
pronounced among those firms that are expected to be most affected by the reform: firms whose
assets are mostly composed of land, buildings, machinery, and equipment. In particular, while
firms in the highest fixed asset quartile observe an average increase in total leverage of 7.2%,
the lowest fixed asset quartile-firms observe no significant changes.
We look at debt maturity to better identify our results. Short-term debt is less likely to be
secured than long-term debt (see Diamond (1991) for theoretical arguments). Crucially, even if
short-term debt is secured, it tends to be secured with current — and not fixed — assets (Ross
et al. (2012)). Conforming with these observations, our evidence shows that the total leverage
increases are entirely driven by increases in long-term leverage. Specifically, while the long-term
leverage of high-fixed asset usage firms rises by 7.8%, the long-term leverage of low-fixed asset
usage firms rises by only 1.2%. By comparison, high- and low-fixed asset usage firms observe
statistically indistinguishable drops in their short-term leverage. A triple differences (DIDID)
estimate of the local effect of Ordonnance 2006-346 on firm debt can be computed as the spread
in the long-term debt change between high- and low-fixed asset firms (7.8%–1.2%) minus the
2
corresponding spread in the short-term debt change (–4.0%–(–2.7%)), which is 7.9%.
In addition to debt levels (the intensive margin), we also study the propensity of firms to
take out any long-term debt at all as a result of the reform (the extensive margin). As stated
before, before 2006, a whooping 90% of all firms have no long-term leverage (“zero-leverage
firms”). However, during the reform year alone, this number drops to 47%. It falls further to
38% at the end of our sample period. Notably, these changes are mostly driven by high-fixed
asset firms. In particular, while the proportion of zero leverage firms in the highest fixed asset
quartile declines from 88% to 32% (a 56% drop), the proportion of zero leverage firms in the
lowest fixed asset quartile declines from 91% to a mere 64% (a 27% drop).
We also identify changes in debt contract terms that facilitated the credit expansion. To do
so, we look at detailed loan contract data. In these tests, we use a more direct identification
strategy. We assign to the treatment group those loans that are directly affected by the reform
(secured loans) and to the control group those loans that are not directly affected by it (unsecured
loans). We show that secured loans become both cheaper and more long-term compared to
unsecured loans after the reform. More specifically, we find that the mark-ups of secured loans
decrease by around 1.2% relative to those of unsecured loans. In addition, the times-to-maturity
of secured loans increase by around 41 months relative to those of unsecured loans.
As a next step, we examine what type of firms were able to enter the corporate debt market
due to the reform. In doing so, we first look at firms that never held any long-term debt before
the reform. We split these firms into those that had a positive long-term leverage ratio in
every post-reform year (“debt switchers”) and those that continued to have zero long-term
leverage in every post-reform year (“non-switchers”). Looking at pre-reform firm characteristics,
the debt switchers are, on average, smaller, more mature firms with more employees than the
non-switchers. Notably, the debt switchers have higher and more stable profits, hold more cash,
and are located further away from the largest French cities than the non-switchers. Moreover,
we report that the reform eased start-up’s access to credit. The mean long-term leverage ratio
of start-ups in their year of incorporation more than doubles from a pre-reform average of 2.7%
to a post-reform average of 5.8%. Also, the proportion of start-ups with no long-term debt in
their year of incorporation decreases from an average of 90% to an average of 74%.
We use data at the Departmental level to investigate the geographic effects of the collateral
3
reform.2 Our evidence shows that the greatest increases in long-term borrowing occurred in rural
areas in the lower center of the country (close to Limousin). Consistent with these findings, landrich high fixed asset-usage firms observed significantly higher increases in long-term leverage
than other high fixed asset-usage firms. We use the Gini index to more formally study whether
the reform reduced inequalities in long-term debt usage. Before the reform, more than nine
out of ten French Departments have a Gini index of long-term debt concentration exceeding
0.90, indicating that debt markets are heavily dominated by a handful of major players. After
the reform, the Gini index drops to an average of around 0.70. Critically, the largest drops are
again observed in the rural Departments close to the lower center of the country.
We use court data at the Departmental level to link the effects of the 2006 reform to court
efficiency. Because the French reform does not allow creditors to bypass inefficient courts (e.g.,
by seizing collateral out-of-court), we anticipate that the reform had more pronounced effects in
Departments with more efficient courts. Our evidence partially supports this hypothesis. While
the length of insolvency proceedings does not condition the magnitude with which high fixed
asset-usage firms increase their long-term borrowing, it significantly lowers the proportion of
firms that start to use long-term debt as a result of the collateral reform.
Looking at the real-side effects of the reform, our tests show that high-fixed asset firms raised
their investment levels more significantly than low-fixed asset firms after the reform. They also
hired more people. Our evidence also suggests that the greater increases in capital and labor
enabled high-fixed asset firms to pursue profitable investments with low risk. Critically, the
changes in profitability and risk experienced by high-fixed assets firms had a more profound
effect on their failure rates than their higher leverage ratios. To be exact, while high-fixed asset
firms were more likely to fail than low-fixed asset firms before 2006, this relationship reversed
after 2006. Thus, taking on more debt seems to be ultimately welfare-enhancing.
Finally, we use industry-level data to study the elasticity between capital growth and valueadded. Our tests show that this elasticity increases significantly from a pre-reform level of 0.43
to a post-reform level of 0.70. Following Wurgler’s (2000) interpretation of this elasticity, our
evidence suggests that the reform increased capital allocation efficiency in France. Consistent
with this evidence, we also provide evidence suggesting that the collateral reform raised long2
The Department is the administrative division between the region and the commune.
4
term borrowing more strongly in industries that are highly dependent on external financing,
possibly implying that the reform helped to lower financial constraints.
We run a number of robustness tests to verify our results. First, we show that high- and
low-fixed asset firms display similar underlying trends in total and long-term leverage before the
reform; the same applies to covariates such as size, profitability, or cash flow volatility. Because
public firms only borrow unsecured, we focus on private firms in our main tests. As such, as a
falsification, we conduct tests using public firms. We confirm that public firms fail to produce
any meaningful patterns in debt taking behavior across firms with different levels of fixed asset
usage. Finally, we repeat our tests on “placebo countries.” We choose as placebo countries
France’s most important civil law neighbors, Belgium, Italy, Portugal, and Spain. Neither of
these countries passed a law similar to Ordonnance 2006-343 in our sample period. Likewise,
neither of them experienced effects similar to those found in France.3
Our study adds to a large literature investigating the effects of creditor rights on economic
outcomes. Since LaPorta et al. (1998), many cross-country studies show that stronger creditor
rights raise capital availability and enhance economic and financial development (e.g., Djankov
et al. (2007), Qian and Strahan (2007), and LaPorta et al. (2008)). Despite these findings, studies
using recent security law reforms as quasi-natural experiments find contradicting evidence. For
example, while Haselmann et al. (2009) and Campello and Larrain (2014) show that reforms in
Eastern Europe increased lending to high quality borrowers, reforms in India and Brazil either
decreased lending or increased lending to the wrong type of borrowers (see Lilienfeld-Toal et
al. (2012), Vig (2013), and Assunção et al. (2014)).
Our study adds to the above studies in two ways. First, it provides further evidence that
security law reforms can lead to an expansion of credit. More importantly, however, it shows
that security law reforms can have important distributional implications. In particular, the
French reform democratized access to credit by allowing young, profitable, previously financiallyconstrained firms in rural areas to borrow long-term, in this way reducing discriminations in
access to debt financing. It also led to an increase in capital allocation efficiency.
Our article is organized as follows. Section 2 gives details about the 2006 French security
law reform. In Section 3, we outline our data and our identification strategy. Section 4 analyzes
3
We also collapse the pre-reform and post-reform data to correct standard errors for autocorrelation in
outcome variables (Bertrand et al. (2004)).
5
the effect of the reform on debt financing. Section 5 identifies those firms that most benefitted
from the reform. Section 6 looks at geographical effects of the reform. In Section 7, we ask to
what use the firms that most strongly increased their borrowing put the newly available debt
capital. Section 8 offers robustness tests. Section 9 concludes.
2
Institutional Setting: Security Laws in France
2.1
The Napoleonic Code of 1804
Prior to the recent reform, security interests in France were governed by the 1804 Napoleonic
Civil Law Code, whose sections dealing with security interests remained essentially unchanged
over the last 200 years. The Napoleonic Code was introduced during a time when France was
recovering from the tumults of the French Revolution. The Revolution transformed France back
into an agricultural country, in which 90% of all property consisted of land.
The French legal system recognizes two forms of security interests, the hypothec and the
charge. A hypothec can only be taken out over real property (land and fixtures), while a charge
can be taken out over all types of assets.4 Given its great importance, security laws at the time
focused on security interests over land — security interests over other assets were deemed as of
little importance (Omar (2007)). Also, the chaos of the revolutionary years convinced regulators
that both borrowers and creditors had to be put into strong positions. In particular, borrowers
had to be protected from exploitive creditors, while creditors had to be protected from borrowers
refusing the surrender of collateral in default (Ancel (2008)). To this end, regulators established
a highly formal, costly procedure to create security rights. Also, they refused to recognize
rechargeable hypothecs (which could lead to conflicts about priority) or non-possessory charges
(which could make it easier for borrowers to not surrender collateral). Ruling out non-possessory
charges implied that security interests over fungible or future assets could also not be allowed
because such assets cannot be physically transferred to creditors. To further protect borrowers,
regulators explicitly forbade out-of-court seizures of collateral in default.
The Napoleonic Code did not promote the creation of a security interest registry covering
assets other than land. To compensate for this deficiency, various local registries emerged over
4
See Boughida et al. (2011) for more details about French security interests.
6
Figure 1. Characteristics of the Old and the New French Security Law Regime This table compares
the old French security law regime based on the 1804 Napoleonic Civil Law Code with the new regime based
on Ordonnance no. 2006-346, enacted on 23 March 2006. The asterisk indicates that the Napoleonic Civil Law
Code allowed for non-possessory security rights over real property (land and fixtures).
time, each covering a narrow range of assets and each restricted to local borrowers. Creditors
had to travel to these registries in person to make inquires (World Bank (2007)).
2.2
Reforming the Napoleonic Code: Ordonnance 2006-346
In the early 2000s, French politicians became increasingly aware of the fact that French security
laws were less competitive than those of other countries, especially those of the so-called common
law countries (Renaudin (2013)). As a result, in March 2005, President Jacques Chirac called
upon the Minister of Justice to reform the country’s security laws. In April 2005, a working
group set up by the Minster of Justice handed in a set of recommendations about reforming
security rights, and in March 2006 parliament enacted a reform based on these.
In addition to combining security laws into a new book in the Napoleonic Code, the 2006
reform also made several more substantial changes. First, it expanded the scope of assets over
which security interests could be taken out by establishing non-possessory charges and security
interests over fungible and future assets (Herbet and Sabbah (2006) and Ancel (2008)). Second,
the reform established an electronic public registry covering all types of assets. Third, the
reform lowered the costs of creating security interests. For example, it reduced the notarial fees,
taxes, and registration costs associated with creating hypothecs. The reform further lowered
transaction costs by allowing security interests to be rechargeable to the same or different
creditors, either simultaneously or consecutively (Herbet and Sabbah (2006)).
In Figure 1, we give an overview of the main differences between the old security law regime
from 1804 and the security law regime introduced in March 2006.
7
2.3
Undermining the Reform: Non-Codified Security Laws
French managers recognized that the 1804 security laws were outdated long before politicians
did. Thus, in the 1970s and 80s, concerted lobbying by them spurred new security laws, which
were not added to the Napoleonic Code. Of these “non-codified laws,” the most popular were
the Dailly Law and the Pledge of Ready Money. The Dailly Law allowed firms to assign their
receivables to financial institutions for security purposes, while the Pledge of Ready Money
allowed them to assign their cash and cash equivalents for similar purposes. Both laws were
remarkably modern insofar as they permitted security to be taken out over fungible and future
assets. Also, they granted super-priority rights to secured creditors in official bankruptcy.5 Their
major limitation was that they were restricted to liquid corporate assets.
Davydenko and Franks (2008) show that the Dailly Law and the Pledge of Ready Money
were popular in France until the 2006 reform. They report that around three quarters of the
collateral used by French firms consisted of “cash collateral” (i.e., cash, guarantees, and accounts
receivables); while three quarters of the collateral used by German and UK firms consisted of
“hard collateral” (i.e., real estate, buildings, and machinery and equipments).
3
Methodology and Data
3.1
Methodology
We employ a DID (alternatively DIDID) methodology to identify the effects of the reform. We
analyze both firm-level and debt contract-level data. In the firm-level analysis, we exploit the
insight that, in contrast to current assets, fixed assets could not be efficiently pledged before the
reform. Consequently, firms operating a large portion of fixed assets would be more significantly
affected by the 2006 reform than firms operating a larger portion of current assets. Accordingly,
we compare the mean of an outcome variable across high- and low-fixed asset firms over the
pre-reform and the post-reform periods. We first calculate the simple mean of the outcome
variable by fixed asset quartile and year. For each fixed asset quartile, we average the yearly
5
Super-priority rights allow secured creditors to extract assets out of the borrowers’ estate before bankruptcy
proceedings begin. Secured creditors are able to do so because, in legal terms, assets covered by super-priority
rights are not property of the bankrupt party, but instead of them.
8
mean of the outcome variable over the pre-reform and the post-reform period and compute the
difference. Subtracting the difference of the lowest fixed asset quartile from that of the highest
quartile gives us the DID estimate for the reform’s effect on the outcome variable.
While the above analysis is intuitive, the resulting estimate is possibly biased due to firms
entering and leaving the sample and firm unobservables. Thus, we also estimate multivariate
models that include firm and year-fixed effects. These models can be written as:
Yi,t = α + αi + αt + βP ostt × T reatedi + Xi,t γ + εi,t ,
(1)
where Yi,t is the value of the outcome variable for firm i in year t, with Yi,t {TotalLeverage,
ShortTermLeverage, LongTermLeverage, DummyNoTotalLeverage, DummyNoShortTermLeverage, DummyNoLongTermLeverage, Growth, Employees, Sales, Profitability, ProfitVolatility}. We
define these variables in the next subsection. P ostt is a dummy variable equal to one during
or after the reform year (2006) and otherwise zero; T reatedi is a dummy variable equal to
one if firm i’s fixed assets-to-total assets ratio averaged over our sample period is in the top
quartile and otherwise zero; and X is a vector of firm-specific controls. αi and αt are firm- and
year-fixed effects, respectively. We cluster standard errors at the firm level.
Note that β in Equation (1) can be interpreted as the regression-based DID estimate after
accounting for the effects of the control variables and firm- and time-invariant effects.
We use an analogous model for the loan-level analysis:
Zi,j,t = α + αk + αt + βP ostt × T reatedi,j,t + Xi,t γ + Wi,j,t δ + εi,j,t ,
(2)
where Zi,j,t is the value of the outcome variable for loan j taken out by firm i in year t, with
Zi,j,t {LoanCost, LoanMaturity}, P ostt is a dummy variable equal to one for secured loans
and otherwise zero, Wi,j,t is a vector of loan-contract specific controls, and αk is an industryfixed effect. We do not use firm-fixed effects in this analysis because roughly 70% of the loan
observations correspond to firms that contribute only one observation to the sample.
9
3.2
Data
To perform our analyses, we use data from several different sources. Our primary source of
financial data is Bureau van Dyck’s AMADEUS Top 250,000. AMADEUS contains comprehensive data on both public and private firms from 35 European countries that satisfy a certain size
threshold.6 We collect data on French firms as well as on firms in other important European
civil law countries (Belgium, Italy, Spain, and Portugal). We use the non-French data to run
placebo tests. In France, annual accounts and other corporate documents are collected and
made available by local commercial court registries (“Greffes des Tribunaux de Commerce”). On
average, Bureau van Dyck requires only one single month to collect the company data and to
include it in AMADEUS once these data become available from the registries.
We exclude firms in the financial (SIC Code 600–699), services (700–899), utilities (490–494),
and public administration (900–999) sectors. Our main tests exclude public firms because these
hardly ever borrow secured. We later run a falsification test on public firms.
To perform the loan contract-level analysis, we merge the AMADEUS database with the
LPC-Dealscan database. We manually match the Dealscan name of each French borrower who
took out a loan in the 2001-2009 period (the “BorrowerIssuerName”) with the AMADEUS
firm name (the “CompanyName”). We succeed in doing so for about 90% of all borrowers. We
only keep revolver and term loans in this analysis because they should be most reflective of a
bank’s pricing terms and its credit restrictiveness (Campello and Gao (2014)).
We use COMPUSTAT and WorldScope data to construct industry-level land, buildings,
and machinery and equipment indexes, and also an industry-level external financial dependence
index. We collect jurisdiction-level data on the duration of insolvency procedures from the
French Ministry of Justice. Finally, we use the United Nations’ General Industrial Statistics
(UNIDO) database (INDSTAT-4) to gather information on gross fixed capital formation and
value added for 124 4 digit-ISIC manufacturing industries.7
6
For France, the database includes all companies that meet at least one of the following criteria: (1) revenues
of at least e15 million, (2) total assets of at least e30 million, and (3) at least 200 employees.
7
See http://www.justice.gouv.fr/statistiques.html.
10
3.3
3.3.1
Variable Construction
Firm-Level Analysis Variables
To examine the effects of the reform on firm-level leverage, we use as outcome variables total,
long-term, and short-term leverage. TotalLeverage is the sum of short-term debt (loan) and longterm debt (ltdb) divided by total assets (toas).8 ShortTermLeverage is defined as short-term debt
divided by total assets, and LongTermLeverage as long-term debt divided by total assets. We
further define three dummy variables, DummyNoTotalLeverage, DummyNoShortTermLeverage,
and DummyNoLongTermLeverage; these are equal to one if the corresponding leverage variable
is equal to zero. We define FixedAssets as fixed assets (fias) divided by total assets.
To study real-side effects, we use as outcomes variables growth, employees, sales, profitability,
and risk. Growth is the change in intangible fixed assets (ifas) plus the change in inventories
(stok), both calculated from the prior fiscal year end to the current one, divided by the average
of total assets over the two fiscal year ends.9 Employees is the log of the number of employees
(emp). Sales is the log of sales. Profitability is earnings before interest and taxes (ebta) divided
by total assets. ProfitVolatility is the standard deviation of Profitability over the four latest
fiscal years. We set ProfitVolatility to zero if it is based on less than three observations.
We use standard controls, including Size and Age. Size is the log of total assets. Age is the
log of the difference between the current year and the year of incorporation.10
To compare the attributes of treated and control firms, we analyze firms’ pre-reform size,
age, number of employees, profitability, risk, cash reserves, capital-to-labor ratio, and levels of
tangible and fixed assets. We also look at their distance to the biggest French cities and to the
Paris Bourse. Cash is cash reserves (cash) divided by total assets. CapitalToLabor is defined
as the log of the ratio of tangible fixed assets to the number of employees.
To calculate the distance variables, we use resources from geonames.org to find the longitude
and latitude associated with each French postal code, including those of the city centers of
the ten largest cities. Using the AMADEUS postal code variable, we then assign longitudes
and latitudes to each firm in our sample. Finally, we use the formula for the spherical distance
8
The items in parentheses are the AMADEUS variable mnemonics.
In France, R&D and formation costs are capitalized under intangible fixed assets.
10
Continuous variables are winsorized at the 1st and 99th percentiles.
9
11
between two points to create the distance variables:
DISTi,j = arccos(deglatlon) × r,
(3)
where DISTi,j is the spherical distance between points i and j, deglatlon is given by:
deglatlon = cos(LATi )cos(LGTi )cos(LATj )cos(LGTj ) + sin(LGTi )sin(LGTj ) +
cos(LATi )sin(LGTi )cos(LATj )sin(LGTj ),
LGTx and LATx are the longitude and latitude of point x, respectively, and r is the radius of
the earth (see Coval and Moskowitz (2001)). We define Distance5 and Distance10 as a firm’s
distance to the city center of the nearest of the five or ten biggest French cities, respectively.
Finally, DistanceBourse is defined as a firm’s distance to the Paris Bourse.
3.3.2
Loan-Level Variables
In the loan-level analysis, we use loan mark-ups and maturity as outcome variables. Spread
is the log of the sum of a loan’s coupon and annual fees scaled by its nominal value minus
the six month LIBOR rate, in basis points. Maturity is the log of the difference between the
loan’s maturity date and its initiation date, in months. We use Secured, a dummy variable
equal to one for secured loans and zero otherwise, to assign firms to treatment. In line with
Chava and Roberts (2008), Roberts and Sufi (2009), and Campello et al. (2011), our control
variables are size, age, profitability, total leverage, the existence of a credit rating, the credit
spread, the term spread, loan size, loan type, and the other endogenous variable. Rating is a
dummy variable equal to one if the firm taking out the loan is rated, else zero. CreditSpread
is the mean of the monthly difference between the yields of a corporate bond index and a
long-term government bond index. TermSpread is the mean of the monthly difference between
the yields of a long-term and a short-term government bond index. Both means are taken over
data from the current year. LoanSize is the log of the notional loan value; and LoanType is a
dummy variable equal to one for term loans and zero for revolver loans.
12
3.3.3
Industry-Level Variables
To identify the types of fixed assets that were used to raise long-term financing, we condition
our tests on the availability of land, buildings, or machinery and equipment. Because we lack
firm-level data on these items, we construct time-invariant indexes at the 4-digit SIC-industry
level using public firms with available data in Compustat and Worldscope. To calculate the
index values, we scale the relevant item (land, buildings, or machinery and equipment) by total
assets, take the mean by industry and fiscal year, and then by industry alone.
Finally, to study capital allocation efficiency, we first use 4-digit ISIC industry-level data for
French manufacturing sectors. InvestmentGrowth is the natural logarithm of the ratio of current
gross fixed capital formation to its one year-lagged value; ValueAddedGrowth is the natural
logarithm of the ratio of current value added to its one year-lagged value.11 Both variables are
deflated using the US GDP deflator. Second, we construct an external financial dependence
index in the spirit of Rajan and Zingales (1998). Following Larrain (2014), we define the index
as the ratio of capital expenditures less cash flow from operations to capital expenditures for
the median public US firm in each 3-digit SIC industry over the 1975-2005 period.
3.3.4
Court Efficiency Variable
We use data from the French Ministry of Justice to measure court efficiency. We proxy for court
efficiency using the average duration of insolvency procedures (restructurings and liquidations),
aggregated up to the Departmental level. HighCourtEfficiency is a dummy variable equal to one
for firms headquartered in Departments in which the average duration is below the country-wide
median, else zero. Because the data required to create HighCourtEfficiency are only available
from 2008, we base the dummy variable on data from this year alone.
11
Gross fixed capital formation is defined as the cost of new and used fixed assets minus the value of sales
of used fixed assets, where fixed assets include land, buildings, and machinery and equipment. Value added
is defined as the value of shipments of goods produced (output) minus the cost of intermediate goods and
required services (but not including labor), with appropriate adjustments made for inventories of finished goods,
work-in-progress, and raw materials.
13
4
Financial Outcomes
4.1
Debt Usage
We first study how the reform affected debt levels and the propensity to have any debt at all.
4.1.1
Total Debt
Table 1 shows that the pre-reform mean total leverage ratio of the sample firms hovers around
9%, without trending upward or downward. However, during the reform year alone, this magnitude shoots up to nearly 12% and continues to rise to 13% in 2009. Comparing mean total
leverage before and after the reform, we find an increase of 3.2%. This increase is equivalent to
35.2% of mean total leverage before the reform and thus economically meaningful. As a result,
the collateral reform had a significant effect on overall borrowing in France.
Ta b l e 1 A b o u t H e r e
Sorting the universe of French firms into fixed asset quartiles, we find that the above increase
in total leverage is entirely driven by firms that make heavy use of fixed assets. Particularly,
while neither high- nor low-fixed asset firms display systematic changes in total leverage before
the 2006 reform, the high-fixed asset firms exhibit far greater jumps in total leverage in the
year of the reform. Comparing mean total leverage before and after the reform, the high-fixed
asset firms observe a 7.2% increase in total leverage, while the low-fixed asset firms observe
only a 0.8% increase. The 6.4% difference, the DID estimate of the collateral reform on total
leverage, is highly statistically significant. Because the difference amounts to 70% of average
pre-reform total leverage, it is economically meaningful as well. Figure 2 is useful in depicting
the effect of the collateral reform on total leverage across different fixed asset-level firms.
4.1.2
Short-term versus Long-term Debt
Next, we split total leverage into short-term and long-term leverage. We do so because shortterm debt is largely unsecured in France, and it is thus not expected to be particularly affected
by the reform. Indeed, while Table 2 shows that short-term leverage declines by around 3.5%
14
Figure 2. Evolution of Mean Total Leverage This figure shows the evolution of the mean total leverage
ratio by fixed asset quartile (Q) over the 2001–2009 period. Q1, Q2, Q3, and Q4 indicate the first, second, third,
and fourth fixed asset quartile, respectively.
over the 2001–2009 period, this decrease is not concentrated in specific years. Notably, we also
find that high- and low-fixed asset firms experience similar declines in short-term debt from the
pre-reform to the post-reform period. In particular, high-fixed asset firms experience a drop
of 4.0%, while low-fixed asset firms experience a 2.7% drop. The difference between these two
numbers, which can be interpreted as the DID estimate for the effect of the collateral reform
on short-term leverage, is not statistically significant.
Ta b l e 2 A b o u t H e r e
In contrast, the patterns observed in long-term leverage closely resemble those observed
in total leverage. While mean long-term leverage remains almost flat at 1.5% until 2005, it
climbs up to an average of 5.3% after the collateral reform. Conditioning on different fixed
asset-levels, we find that high-fixed asset firms experience far larger increases in long-term debt
than low-fixed asset firms. While the mean long-term leverage of firms in the highest fixed asset
quartile rises by 7.8%, the lowest fixed asset quartile firms observe an increase of only 1.2%.
The 6.6% spread, the DID estimate for the effect of the collateral reform on long-term debt,
is highly statistically significant. Since this spread is a striking 440% of the average value of
long-term leverage before the reform, it is also highly economically important.
Figure 3 depicts the monotonic impact of the reform on the short-term and long-term
leverage of firms within the different fixed asset quartiles.
15
Figure 3. Evolution of Mean Short-term vs.Mean Long-term Leverage This figure shows the evolution
of mean short-term and mean long-term leverage by fixed asset quartile (Q) over the 2001–2009 period. Q1, Q2,
Q3, and Q4 indicate the first, second, third, and fourth fixed asset quartile, respectively.
4.1.3
Zero Leverage Firms
We next study the effects of the 2006 reform on the distribution of short-term and long-term
debt. To do so, we analyze the proportion of firms with no short-term or long-term debt. Table 3
shows that the proportion of zero short-term debt firms is relatively stable over time for the
whole sample, and also all four fixed asset-subsamples. As a result, the difference in the changes
observed by the highest- and lowest fixed asset firms, the DID estimate of the effect of the
reform on the proportion of zero short-term leverage firms, is an insignificant 0.3%.
In sharp contrast, the proportion of zero long-term debt firms is around 90% before the
reform, but drops to 47% in the reform year and continues to decrease to 38% in 2009. As
before, high-fixed asset firms are most responsible for this pattern. In particular, while the
proportion of zero long-term debt firms in the lowest-fixed asset quartile drops by only 27%,
the corresponding drop in the highest-fixed asset quartile is more than double, 56%. The DID
estimate for the effect of the reform on the proportion of zero long-term leverage firms is
statistically and economically significant (–56%–(–27%)=–29%).
Ta b l e 3 A b o u t H e r e
In Figure 4, we graphically show the impact of the reform on the proportion of zero shortterm or long-term leverage firms across firms with different levels of fixed asset-usage.
16
Figure 4. Evolution of the Proportion of Zero Short-term vs. Zero Long-term Leverage Firms
This figure shows the evolution of the proportion of zero short-term and zero long-term leverage firms by fixed
asset quartile (Q) over the 2001–2009 period. Q1, Q2, Q3, and Q4 indicate the first, second, third, and fourth
fixed asset quartile, respectively.
4.1.4
Regression Results
Table 4 reports the results of DID regressions that correspond to the ANOVA tests in Tables 1
through 3. In the first three columns, we employ as outcome variables total, short-term, and
long-term leverage; in the next three columns, we employ dummy variables equal to one if total,
short-term, or long-term debt is zero and else zero (“zero-leverage firms”). Even after including
control variables and firm and year-fixed effects and keeping assignment to treatment fixed, our
results are essentially identical to the differences in means in Tables 1–3. The control variables
are also highly significant with the expected signs.
Ta b l e 4 A b o u t H e r e
The DID regressions confirm that Ordonnance 2006-346 led to a significant increase in
long-term borrowing by firms that operate more fixed assets (intensive margin). In addition,
the proportion of high fixed-asset usage firms with no long-term debt decreases sharply with
that same reform (extensive margin). In contrast, low-fixed asset firms do not significantly
increase their long-term borrowing, and the proportion of zero long-term debt firms among
them drops far less significantly. Albeit statistically weak, our evidence suggests that high-fixed
asset-usage firms even substitute short-term debt for long-term debt after the reform.
17
4.2
Debt Contracting Terms
We investigate whether debt contract terms change following the reform. To this end, we focus
on the pricing and maturity terms of secured (treated) and unsecured (control) loans before and
after the reform. The DID regressions in Table 5 show that secured loans become significantly
cheaper and more long-term compared to unsecured loans after the reform, independent of
whether we control for firm characteristics. In particular, the log all-in-drawn spread of secured
loans decreases by around 1.30 more than the same spread for unsecured loans across the two
models. This decrease is equivalent to a 120 basis points greater reduction in loan costs. At the
same time, the log time-to-maturity of secured loans increases by around 0.83 more than that
of unsecured loans across the two models. This decrease is equivalent to a 41 month greater
increase in the time-to-maturity. The estimates on the control variables are consistent with
those found elsewhere (e.g., Chava and Roberts (2008)). Overall, our evidence suggests that
the reform made banks more willing to extend secured credit on more favorable terms.
Ta b l e 5 A b o u t H e r e
5
Which Firms Were Most Affected By the Reform?
5.1
Characteristics of Debt Switchers and Non-Switchers
Previous work shows that collateral reforms sometimes only benefit certain types of firms, and
not always those in need of a better access to capital (e.g., Assunção et al. (2014)). Thus, as a
next step, we examine which type of firms experienced the greatest increases in borrowing due
to the reform. We compare the attributes of those firms that most significantly increased their
borrowing following the reform with the attributes of those firms that did not. To this end, we
focus on the subsample of firms with zero long-term leverage in every pre-reform year. We split
these firms into two groups, one including those firms that have a positive long-term leverage
ratio in every year after the reform (“debt switchers”) and another one including firms that
keep a zero long-term leverage ratio every year after the reform (“non-switchers”).
Table 6 reveals that the debt switchers are smaller, but slightly more mature firms than
the non-switchers. Debt switchers, on average, employ approximately 20 more people than the
18
non-switchers. Also interestingly, they have a 1.5% higher profitability and a 1.2% lower profit
volatility than the non-switchers. Finally, they hoard less cash, have higher capital-to-labor
ratios, and are more tangible and fixed asset-intensive than the non-switchers. Notably, the
debt switchers tend to be located further away from the five or ten biggest French cities and
from the Paris Bourse than the non-switchers.
Ta b l e 6 A b o u t H e r e
Overall, our evidence shows that the collateral reform incentivized creditors to extend longterm financing to young, profitable firms with a low risk. It also suggests that the firms that
benefitted most from the reform were located far away from the big cities.
5.2
The Financing of Start-Ups
A large literature shows that access to capital helps to foster entrepreneurship (Evans and
Leighton (1989) and Holtz-Eakin et al. (1994)). For example, Bertrand et al. (2007), Kerr and
Nanda (2009), and Chatterji and Seamans (2012) show that deregulation of the banking sector
or credit card markets leads to a higher rate of new businesses formation. They argue that these
findings are driven by the fact that deregulating leads to a reduction in financial constraints,
making prospective entrepreneurs more willing to compete with incumbents firms.
In Table 7, we show that entrepreneurs in France also benefitted from the 2006 collateral
reform. Before the reform, French start-ups had an average long-term leverage between 1.9%
and 3.6% in their year of incorporation. However, this figure increases to 4.8% in the reform
year, and it continues to increase to 7.2% in 2009. Similarly, before the reform, 90% of all
start-ups had no long-term debt in their year of incorporation. However, this figure drops to
74% after the reform. Critically, a firm’s fixed asset-usage again conditions these results. In
particular, the firms in the highest-fixed asset quartile show a 5.2% greater increase in their
initial long-term leverage than firms in the lowest-fixed asset quartile.
Ta b l e 7 A b o u t H e r e
19
Figure 5. The Long-term Leverage Distribution of French Departments Before and After the
Reform The histograms show the distributions of mean long-term leverage and the proportion of no long-term
leverage firms of the 96 French Departments both for the pre-reform and the post-reform period. The histogram
entries are the mean of long-term leverage and the proportion of zero long-term leverage firms by Department
and year, separately averaged over the pre-reform and the post-reform years.
6
The Geography of the Collateral Reform
6.1
Financing Effects of the Reform Across Departments
Lilienfeld-Toal et al. (2012) find evidence that security law reforms can redistribute credit from
firms headquartered in rural areas to those headquartered in larger cities. These findings raise
concerns about the distributional effects of security law reforms because firms in larger cities
have more options to raise external capital than those in rural areas.
To study the geographical effects of the reform, we proceed in two steps. First, we identify
the Departments that benefitted most from the reform. To wit, we calculate mean long-term
leverage and the proportion of zero long-term leverage firms by Department and year. We
separately average these measures over the pre-reform and the post-reform years. Figure 5
shows histograms of the distributions of Departments’ mean long-term leverage (Panel A) and
proportions of zero long-term debt firms (Panel B) before and after the reform. The histograms
reveal that, before the reform, the vast majority of Departments have a mean long-term leverage
20
between 1% and 3% and a mean proportion of zero long-term leverage firms between 60% and
90%. However, these figures change significantly following the reform. In particular, the reform
shifts the bulk of the mean long-term leverage distribution to the right, so that after the reform
most Departments have a mean long-term leverage ratio between 4% and 9%. Similarly, it shifts
the bulk of the mean proportion of zero long-term leverage firms distribution to the left, so that
after the reform most Departments have a mean proportion of zero long-term leverage firms
between 0% and 40%. Thus, our empirical evidence suggests that the 2006 reform benefitted
firms all over France, and not just those located in specific areas.
Second, we analyze how specific Departments were affected by the reform. In doing so, we
create for each Department the change in long-term leverage from before to after the reform
and the proportion of firms that never had any long-term leverage before the reform, but that
reported a positive long-term leverage every year after the reform (”debt switchers”). Figure 6
plots these changes. The darker a Department’s color in map the more the Department is
affected. The yellow-rimmed Departments are those in which the ten largest French cities
are located. The maps show that the largest increases in long-term leverage and the largest
decreases in the proportion of zero long-term leverage firms occurred far away from the big
cities. Thus, not only did the reform abstain from favoring firms headquartered in big cities, it
actually channeled the largest amount of new capital to firms in rural areas.
6.2
What Type of Fixed Assets Were Used As Collateral?
Our results show that the collateral reform led to the largest increase in long-term borrowing
in rural areas with a low population density. Because firms located in these areas own a great
amount of land, we conjecture that the collateral reform enabled these firms to pledge their
land and thereby to secure long-term debt financing. To test this hypothesis, we proceed as
follows. Within each fixed-assets quartile, we further sort firms into either land-, buildings-, or
machinery and equipment-index quartiles. Then, for each double-sorted subgroup of firms, we
calculate the average long-term leverage ratio before and after the reform.
Table 8 shows the mean long-term leverage ratio of the extreme fixed asset-fixed asset
type-subgroups over the pre-reform and post-reform period. The table confirms that land-rich
high fixed asset-usage firms were able to raise more long-term debt financing due to the reform
21
Figure 6. Change in Mean Long-term Leverage and Proportion of Debt Switchers by Department
In Panel A, we show the change in mean long-term leverage from the pre-reform to the post-reform period for
each French Department. Mean long-term debt is long-term leverage, first averaged by Department and year
and then by Department and pre-reform or post-reform period. In Panel B, we show the proportion of firms
that had no long-term debt in any year preceding the reform, but that had a positive long-term leverage ratio
in every year proceeding the reform. The darker a Department’s color in the maps, the greater is either the
change in mean long-term leverage (Panel A) or the proportion of debt switchers (Panel B). The yellow-rimmed
Departments in the maps are those containing the ten largest French cities.
than land-poor high fixed asset-usage firms. In particular, only looking at firms in the highest
land index quartile, the difference in the increase in the mean long-term leverage ratio across
the highest and lowest fixed asset quartiles, the DID effect of the reform among land-rich firms,
is 10.0%. In contrast, among firms in the lowest land index quartile, the same difference is only
4.9%. Taken together, these figures imply a DIDID effect of 5.1% (10.0%–4.9%). If we instead
use the buildings index or the machinery and equipment index as second sorting variable, we
find DIDID estimates of 4.0% (9.1%–5.1%) and 1.2% (5.6%–4.4%), respectively. Thus, while
firms also used their buildings to secure new debt, there is only very weak evidence to suggest
that they used their machinery and equipment for the same purposes.
Ta b l e 8 A b o u t H e r e
6.3
Inequalities in Access to Long-term Debt
Our results show that the collateral reform produced the largest increase in long-term borrowing
among firms that are often expected to have less access to capital markets, such as small firms
or firms located in rural areas. To further support the idea that the 2006 reform spurred a
democratization of credit, we use as an alternative measure the Gini index for long-term debt
22
Figure 7. Pre- and Post-Reform Long-term Debt-Gini Index By Department The maps show the
long-term debt-Gini index of French Departments for the pre-reform (Panel A) and the post-reform (Panel B)
period. To calculate the Gini index, we create the Gini index by Department and year and then average by
Department and pre-reform or post-reform period. The darker a Department’s color in the maps, the greater is
its Gini index. The yellow-rimmed Departments in the maps are those containing the ten largest French cities.
usage at the Departmental level. We calculate the Gini index as follows:
GiniIndexk,t
Nk,t Nk,t
X
X
1
|LongT ermLeveragei,t − LongT ermLeveragej,t |,
=
2
2µk,t Nk,t
i=1 j=1
(4)
where GiniIndexk,t is Department k’s Gini index in year t, µk,t is the average long-term leverage
of the firms in Department k, i and j are firm indexes, and Nk,t is the number of firms. Next, we
average GiniIndexk,t by Department and year, and then by Department and pre- or post-reform
period. The calculated Gini index ranges from zero to one, and a higher value indicates a more
unequal distribution of long-term debt usage across firms.
Figure 7 shows each Department’s pre- and post-reform Gini index value. The darker a
Department’s color in the maps, the higher is its Gini index. Before the reform, only six out of
96 Departments observe a Gini index value below 0.90, suggesting that long-term debt is heavily
concentrated in the hands of a few firms. However, following the reform, the Gini index drops
significantly to an average value of 0.72. While all Departments observe profound decreases
in their Gini index values, it is again rural Departments that experience the most dramatic
changes. Overall, the collateral reform greatly decreased inequalities in the access to credit,
and the greatest improvements took place in the country-side.
23
6.4
Court Efficiency
We investigate whether court efficiency conditions the debt financing effects of the collateral
reform. Campello and Larrain (2014) show that a Romanian security law reform led to greater
expansions in corporate credit-taking in regions hosting less efficient courts. This arises because
the Romanian reform enabled secured creditors to seize collateral out-of-court, thereby allowing
them to bypass inefficient courts. While formally the French reform also removed the ban on
out-of-court seizures, French insolvency laws continue to stay such rights in bankruptcy. Thus,
secured creditors in France remain highly dependent on courts. This may imply that the effects
of Ordonnance 2006-343 are stronger in areas hosting efficient courts.
We use DIDID regressions to study the intermediating effect of court efficiency. The endogenous variables are the same as in Table 4. As exogenous variables, we use the same variables as
in Table 4 plus: the double-interaction between Post and HighCourtEfficiency and the tripleinteraction between Post, Treated, and HighCourtEfficiency. The DIDID regressions contain
firm- and year-fixed effects; standard errors are clustered at the firm-level. The coefficient on
the triple interaction gives the effect of court efficiency on the reform’s debt effects.
Table 9 offers partial support for our hypothesis. While court efficiency does not condition
the increase in leverage levels, it has a significant impact on the decline in the proportion of zero
long-term leverage firms. In particular, while the proportion of zero long-term leverage firms
drops by 10.0% in Departments hosting less efficient courts, it drops by 13.4% in Departments
hosting more efficient courts. The difference is highly statistically significant.
Ta b l e 9 A b o u t H e r e
7
Real Outcomes
We also assess how the firms that most increased their borrowing after the reform used the new
credit that they were able to tap. This is interesting since the higher leverage ratios could simply
lead to a higher default risk in the economy. On the other hand, if the firms that increased
their borrowing invested the proceeds from the new debt into profitable low risk-projects, the
leverage-induced increase in default risk could be dominated by profitability- and volatility-
24
induced declines in default risk. It is an empirical question how the reform affected the default
risk of the firms that experienced the largest increases in access to collateral.
7.1
Performance
To address the above question, Table 10 reports the results of DID regressions using as outcome
variables investment, employees, sales, profitability, and profit volatility. Our evidence shows
that the firms that most benefitted from the collateral reform (i.e., firms with a lot of fixed
assets) increased investment more and hired more people after the reform. For example, highfixed asset firms experienced an around 4.9% higher employment growth than low-fixed asset
firms. In addition, firms with a lot of fixed assets raised their output more and became more
profitable and less risky than others after the reform. For example, those firms experienced a
4.5% higher sales growth than low-fixed asset firms after the reform.
Ta b l e 1 0 A b o u t H e r e
7.2
Survival Analysis
We use a Cox proportional hazard model to fit the number of years until a firm fails. The Cox
proportional hazard model assumes that the hazard rate is given by:
λi,t = φt exp (βP ostt × T reatedi + Xi,t γ) ,
(5)
where the hazard rate, λi,t , is defined as the probability of firm i failing at time t conditional
on surviving until then. We back out the year in which a firm fails using the “legal status”
variable in AMADEUS. We set the failure year equal to the calendar year during which a firm’s
legal status changes from one of the active statuses to one of the failure statuses (if any).12 We
exclude firms from the hazard analysis for which it is unclear whether they became inactive for
performance reasons. φt is the “baseline” hazard rate common to all firms, and the exponential
function allows for cross-sectional variations in hazard. Using a partial likelihood estimator,
we are able to obtain estimates of β and γ without imposing any structure on φt . The same
12
The failure statuses are: “default of payment,” “insolvency proceedings,” “receivership,” “bankruptcy,”
“dissolved (bankruptcy),” “dissolved (liquidation),” and “in liquidation.”
25
estimator is also able to account for right-censoring of data (i.e., firms that do not fail within
the sample period or that leave the sample for reasons unrelated to performance).
In Table 11, we show the results from the Cox proportional hazard models. In the absence
of controls, firms with a lot of fixed assets experience a 28.6% (e−0.338 ) greater decrease in their
failure rates than other firms after the reform. Controlling for size and cash does not influence
these findings. However, controlling for leverage and thus for the fact that high-fixed asset
firms increased their debt levels more than others (implying that they could have become more
distressed than others), the difference in the change in failure rates between high- and low-fixed
asset firms widens. Finally, adding profitability and profit volatility more than halves this
difference. The implication is that the high-fixed asset firms’ greater increases in profitability
and greater declines in profit volatility partially explain why their failure rates decline relative
to the failure rates of low-fixed asset firms after the reform.
Ta b l e 1 1 A b o u t H e r e
7.3
Capital Allocation Efficiency
One of the ultimate goals of financial markets in general, and banks in particular, is to allocate
capital efficiently (Schumpeter (1934), Diamond (1984), and Morck et al. (2011)). Given their
unique advantage in acquiring and processing information to assess growth opportunities, banks
have a unique advantage in doing so. However, the institutional setting and legal framework can
significantly affect how banks operate (La Porta et al. (1998)). Our previous findings show that
the 2006 reform made banks less reluctant to lend to profitable, less risky firms. The ultimate
question then is whether this lower reluctance brought about a more efficient allocation of
capital in the economy. To answer this question, we use two different tests.
First, we follow Wurgler (2000) in reasoning that for the capital allocation process to be
efficient firm-level investments should increase in sectors with better growth opportunities and
decrease in sectors with worse growth opportunities. Using value added-growth as a proxy for
growth opportunities, we use the sensitivity of capital investment to the growth in value-added
as a measure of the efficiency of capital allocation, and we study whether this sensitivity has
increased after the reform. In particular, we run the following regression on industry-level
26
panel-data from before and from after the reform:
InvestmentGrowthk,t = α + βV alueAddedGrowthk,t + εk,t ,
(6)
where InvestmentGrowthk,t is gross fixed capital formation and V alueAddedGrowthk,t the
value-added of industry k in year t. Using pre-reform data, Table 12 shows that the elasticity
between investment and value-added (β) is 0.43. Using post-reform data, it becomes 0.70. The
difference between the two numbers, 0.27, is statistically significant at the 10% level (t-statistic:
1.79). Based on Wurgler’s (2000) interpretation of these elasticities, the reform thus led to a
statistically and economically significant increase in capital allocation efficiency.
Ta b l e 1 2 A b o u t H e r e
Second, we use the external financial dependence measure proposed by Rajan and Zingales
(1998) as alternative proxy for capital allocation efficiency. This measure captures an industry’s
technological demand for external financing. In an efficient economic system, capital should
be directed towards those sectors that are more reliant on external financing. Thus, if the
2006 reform relaxed lending constraints, we should observe a significantly larger increase in
borrowing among firms operating in sectors highly dependent on external financing. To test this
hypothesis, we split our sample into two groups, one including those with a high dependence on
external financing (proxy value above median) and one including those with a low dependence
(proxy value below median). Next, we split the firms in the high and low dependence-subsamples
into treated and control firms, where treatment status is assigned as before.
Considering all firms, Table 13 shows that firms with a high dependence on external financing
observed greater increases in long-term leverage from the pre-reform to the post-reform period
than other firms. In particular, while the mean long-term leverage ratio of highly dependent
firms increases by 4.8%, the mean long-term leverage ratio of firms with a weaker dependence
increases by only 2.9%. The difference is highly statistically significant. Restricting our attention
to treated firms, the difference widens. In particular, while highly dependent high fixed assetusage firms observe a mean increase in their long-term leverage of 8.2%, the mean long-term
27
leverage of less dependent high fixed asset-usage firms increases by only 5.3%.
Ta b l e 1 3 A b o u t H e r e
Overall, these two tests suggest that the collateral reform helped the French economic system
move towards allocating more capital to high-value added sectors and to sectors that rely more
on external financing.
8
Robustness
8.1
Self-Selection and Parallel Trends
The validity of DID tests rests on a number of assumptions, of which the most important one
is that firms cannot self-select into treatment. In our context, this implies that we need to rule
out that firms manage their assets in such a way that they always benefit from the prevailing
security law regime. Results in Table 14 suggest that it is unlikely that firms manage their
assets in such a way. Over the 2001–2005 period, a mere 6.8% of firms moved from the lower
three fixed asset quartiles into the highest one; indistinguishable from the 7.0% that moved into
the other direction. Notably, the 2001–2005 migration rates do not differ much from migration
rates calculated over other five year-periods within or before our sample period.
Ta b l e 1 4 A b o u t H e r e
If the random assignment to treatment-assumption is fulfilled, treated and control firms
should display parallel trends in outcome variables before the reform. We have verified that
this is the case in Figures 1 and 2, in which neither treated nor control firms observe any significant trends in total or long-term leverage during the period preceding the reform. Additional
statistical tests confirm this result (available from the authors upon request).
28
Figure 8. Evolution of the Proportion of Zero Long-term Leverage Firms By Country The bar
chart shows the proportion of zero long-term leverage firms in France and each of the placebo countries (Belgium,
Italy, Spain, and Portugal) over the 2001–2009 period.
8.2
Autocorrelation in Outcome Variables
Bertrand et al. (2004) show that autocorrelation in the outcome variable can create upwardbiased inference levels in DID regressions. Following their work, we separately average each
analysis variable over the pre- and the post-reform period and then repeat the DID regressions
using the collapsed data. Results are virtually identical to those reported in Table 4.
8.3
8.3.1
Placebo Tests
Public Firms
Wood (2007) argues that public firms only borrow secured under exceptional circumstances,
for example, when they are distressed or borrow for project financing purposes. Thus, we also
repeat our tests on public firms, expecting these to be less affected by the reform than private
firms. Table 15 confirms this intuition. The table shows that, while public firms also experience
a slight increase in long-term leverage from the pre-reform to the post-reform period, we find
no evidence to suggest that this increase is driven by high-fixed asset firms. If anything, results
work in the opposite direction. Specifically, while firms in the lowest fixed asset quartile increase
their long-term leverage by an average of 4.2%, those in the highest fixed asset quartile slightly
29
decrease it by an average of 0.9%, with the spread, however, not attaining statistical significance.
Running DID regressions on public firms confirms these conclusions (not reported).
Ta b l e 1 5 A b o u t H e r e
8.3.2
Other Civil Law Countries
As a final falsification test, we repeat our tests on European civil law countries that did not
reform their security laws during the sample period. These countries are Belgium (a neighbor
with the same language and close cultural and economic ties), Italy, Spain, and Portugal. Figure 8
uses bar charts to graphically illustrate the results of these falsification tests. The figure displays
the proportion of zero long-term leverage firms for France and each placebo country over the
2001–2009 period. The most important conclusion we draw from Figure 8 is that none of the
placebo countries experiences a decrease in zero long-term debt firms as dramatic as the one
experienced by France. Also interesting is that the security law reform makes the proportion of
zero long-term leverage firms in France far more similar to those in the other countries, which
already had efficient security laws in place at the start of our sample period.
In Figure 9, we offer line charts showing the mean value of long-term leverage and the
proportion of zero long-term leverage firms per placebo country. We again conclude that none
of the placebo countries displays effects similar to those found in France. DID regressions using
data from the placebo countries confirm this conclusion (not reported).
9
Conclusion
In recent years, many countries have undertaken reforms of their security laws, often under the
banner of “enhancing access to credit.” Consistent with this aim, prior research has shown that
such reforms can raise the volume of corporate borrowing (Vig (2013), Assunção et al. (2014),
and Campello and Larrain (2014)). However, raising the volume of corporate borrowing is only
the tip of the iceberg. More than on how much new debt capital large, established companies
can raise, the success of these reforms hinges on whether they open up credit for companies that
were hitherto locked out of debt markets. Focusing on financially-constrained firms — small
and young firms, start-ups, firms located in the country-side — is important because these
30
Figure 9. Evolution of Mean Long-term Leverage and the Proportion of Zero Long-term Leverage
Firms in the Placebo Countries The figure shows the evolution of mean long-term leverage (upper panels)
and the proportion of zero long-term leverage firms (lower panels) for each placebo country and fixed asset
quartile (Q) over the 2001–2009 period. The placebo countries are Belgium, Italy, Spain, and Portugal. Q1, Q2,
Q3, and Q4 indicate the first, second, third, and fourth fixed asset quartile, respectively.
firms tend to generate the lion share of most countries’ value-added.
Compared to prior studies, we offer a more comprehensive analysis of the effects of collateral
reforms. In particular, we do not only study the impact that such reforms have on the magnitude
of debt financing, but we also look at their impact on the composition of corporate debt, the
distribution of corporate debt usage across firms, and capital allocation efficiency. To achieve
these goals, we consider a security law reform that was recently implemented by a developed
country. This reform is Ordonnance 2006-346, enacted in France in March 2006. Looking at
a reform in a developed country has the advantage that we have more data at our disposal,
implying that we can study the reform from more angles than other studies. Our focus on
France is also helpful to shed light on the distributional effects of collateral reforms. Up to the
recent collateral reform, access to credit was highly unequally distributed across French firms,
with a handful of major companies holding all the long-term debt capital.
Our evidence shows that the firms expected to be most affected by the reform raised their
long-term leverage from a pre-reform average of 2.4% to a post-reform average of 10.2%. Also,
the fraction of zero long-term debt firms among them dropped from a pre-reform average of
88% to a post-reform average of 32%. In contrast, we find no discernable patterns in these
variables among the control firms. Loan contract data suggest that the above credit expansions
were driven by cheaper and more long-term secured credit. Thus, we conclude that the reform
31
made debt capital more attractive and raised overall corporate borrowing.
However, our evidence also speaks to how the reform changed the demographics of corporate
debt usage. In particular, we show that a large amount of the newly available capital went to
small, profitable firms that are located in rural areas. A large fraction of the newly-raised debt
financing was secured using land, explaining why the increases in long-term debt financing are
especially pronounced among firms located in rural areas. The reform also led to increases in
long-term debt usage among start-ups. By opening up access to credit, the reform produced
significant decreases in the Gini index of long-term debt concentration all over the country.
Overall, our evidence suggests that the reform produced a “democratization of corporate debt.”
Finally, firms used the new debt financing in social welfare-enhancing ways. In particular, we
show that the firms expected to be most affected by the reform became more profitable, less risky,
and less likely to fail than others after the reform. Also, the reform led to a significant increase
in the elasticity between investment and value-added and to significantly more borrowing to
firms operating in industries heavily reliant on external financing. Both these findings could
point to reform-induced improvements in the efficiency with which capital is allocated.
32
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34
Table 1
Effect of the Reform on Total Leverage: ANOVA Tests
This table shows mean total leverage by year and fixed asset quartile over the 2001–2009 period. Total leverage
(“TotalLeverage”) is defined as the sum of short-term and long-term debt scaled by total assets. Fixed assets
(“FixedAssets”) is defined as fixed assets scaled by total assets. We sort firms into fixed asset quartiles according
to the values of fixed assets and the fixed asset quartile breakpoints in the current year. Quartile Q1 contains
firms with a low-fixed asset ratio, while Quartile Q4 contains firms with a high ratio. At the bottom of the
table, we report averages for the pre-reform period means (2001–2005) and averages for the post-reform period
means (2006–2009), and their differences. The final column shows the differences in means across the highest
(Q4) and lowest (Q1) fixed asset quartile. ∗∗∗ , ∗∗ , and ∗ indicate statistical significance at the 99%, 95%, and
90% confidence levels, respectively, based on standard errors clustered at the firm level.
Fixed Asset Quartile
Year
All
2001
Diff.
Q1
Q2
Q3
Q4
0.098
0.078
0.092
0.102
0.120
0.043***
2002
0.095
0.073
0.089
0.098
0.118
0.044***
2003
0.091
0.069
0.085
0.095
0.113
0.045***
2004
0.086
0.063
0.078
0.089
0.113
0.050***
2005
0.088
0.067
0.078
0.090
0.115
0.049***
2006
0.116
0.077
0.096
0.119
0.172
0.095***
2007
0.120
0.077
0.098
0.121
0.183
0.106***
2008
0.128
0.080
0.106
0.128
0.197
0.116***
2009
0.130
0.079
0.105
0.135
0.202
0.123***
Mean 2001–2005 (1)
Mean 2006–2009 (2)
0.091
0.123
0.070
0.078
0.085
0.101
0.095
0.126
0.116
0.188
Diff. (2)–(1)
0.032***
0.008***
0.016***
0.031***
0.072***
35
Q4–Q1
0.064***
Table 2
Effect of the Reform on Mean Short-term and Long-term Leverage: ANOVA Tests
This table shows mean short-term and mean long-term leverage by year and fixed asset quartile over the 2001–
2009 period. The upper number in each row is short-term (ST) leverage, while the lower number is long-term
(LT) leverage. Short-term leverage (“ShortTermLeverage”) is defined as short-term debt scaled by total assets,
whereas long-term leverage (“LongTermLeverage”) is defined as long-term debt scaled by total assets. Fixed
assets (“FixedAssets”) is defined as fixed assets scaled by total assets. We sort firms into fixed asset quartiles
according to the values of fixed assets and the fixed asset quartile breakpoints in the current year. Quartile Q1
contains firms with a low-fixed asset ratio, while Quartile Q4 contains firms with a high ratio. At the bottom of
the table, we report averages for the pre-reform period attributes (2001–2005) and averages for the post-reform
period attributes (2006–2009), and their differences. The final column shows the differences in attributes across
the highest (Q4) and the lowest (Q1) fixed asset quartile. ∗∗∗ , ∗∗ , and ∗ indicate statistical significance at the
99%, 95%, and 90% confidence levels, respectively, based on standard errors clustered at the firm level.
Debt
Year
Type
2001
Fixed Asset Quartiles
Diff.
All
Q1
Q2
Q3
Q4
Q4–Q1
ST
LT
0.080
0.016
0.067
0.009
0.078
0.013
0.084
0.017
0.090
0.026
0.023***
0.017***
2002
ST
LT
0.077
0.015
0.062
0.010
0.076
0.012
0.082
0.015
0.090
0.024
0.028***
0.014***
2003
ST
LT
0.074
0.014
0.059
0.008
0.073
0.011
0.079
0.015
0.086
0.023
0.027***
0.015***
2004
ST
LT
0.070
0.013
0.055
0.006
0.069
0.009
0.073
0.014
0.084
0.023
0.030***
0.017***
2005
ST
LT
0.071
0.015
0.057
0.008
0.068
0.010
0.074
0.016
0.085
0.025
0.028***
0.018***
2006
ST
LT
0.071
0.042
0.059
0.016
0.067
0.028
0.074
0.044
0.085
0.082
0.026***
0.066***
2007
ST
LT
0.071
0.046
0.059
0.016
0.068
0.029
0.072
0.048
0.085
0.093
0.027***
0.077***
2008
ST
LT
0.068
0.058
0.055
0.023
0.067
0.037
0.070
0.057
0.080
0.113
0.024***
0.090***
2009
ST
LT
0.063
0.065
0.050
0.027
0.062
0.041
0.065
0.069
0.076
0.122
0.026***
0.095***
Mean 2001–2005 (1)
ST
LT
0.103
0.015
0.083
0.008
0.101
0.011
0.109
0.015
0.121
0.024
Mean 2006–2009 (2)
ST
LT
0.068
0.053
0.056
0.020
0.066
0.034
0.070
0.055
0.082
0.102
Diff. (2)–(1)
ST
LT
-0.035***
0.038***
-0.027***
0.012***
36
-0.035***
0.023***
-0.039***
0.039***
-0.040***
0.078***
-0.013
0.066***
Table 3
Effect of the Reform on the Proportions of Zero Short-term and Long-term Leverage Firms:
ANOVA Tests
This table shows the fraction of zero short-term and long-term leverage firms by year and fixed asset quartile
over the 2001–2009 period. The upper number in each row is short-term (ST) leverage, the lower number
long-term (LT) leverage. Short-term leverage (“ShortTermLeverage”) is defined as short-term debt scaled by
total assets; long-term leverage (“LongTermLeverage”) is defined as long-term debt scaled by total assets. Fixed
assets (“FixedAssets”) is defined as fixed assets scaled by total assets. We sort firms into fixed asset quartiles
according to the values of fixed assets and the fixed asset quartile breakpoints in the current year. Quartile Q1
contains firms with a low-fixed asset ratio, while Quartile Q4 contains firms with a high ratio. At the bottom of
the table, we report averages for the pre-reform period attributes (2001–2005) and averages for the post-reform
period attributes (2006–2009), and their differences. The final column shows the differences in attributes across
the highest (Q4) and the lowest (Q1) quartile. ∗∗∗ , ∗∗ , and ∗ indicate statistical significance at the 99%, 95%,
and 90% confidence levels, respectively, based on standard errors clustered at the firm level.
Debt
Year
Type
2001
Fixed Asset Quartile
Diff.
All
Q1
Q2
Q3
Q4
Q4-Q1
ST
LT
0.169
0.886
0.264
0.898
0.141
0.883
0.126
0.883
0.145
0.878
-0.119***
-0.020***
2002
ST
LT
0.172
0.893
0.280
0.903
0.145
0.893
0.123
0.890
0.141
0.886
-0.139***
-0.017***
2003
ST
LT
0.176
0.898
0.278
0.913
0.154
0.893
0.122
0.895
0.149
0.890
-0.129***
-0.023***
2004
ST
LT
0.180
0.902
0.293
0.919
0.152
0.909
0.128
0.889
0.145
0.890
-0.149***
-0.029***
2005
ST
LT
0.177
0.875
0.287
0.910
0.149
0.874
0.130
0.859
0.140
0.858
-0.147***
-0.052***
2006
ST
LT
0.172
0.470
0.277
0.683
0.152
0.441
0.121
0.380
0.138
0.376
-0.139***
-0.306***
2007
ST
LT
0.173
0.421
0.276
0.661
0.149
0.396
0.128
0.319
0.137
0.310
-0.139***
-0.351***
2008
ST
LT
0.212
0.397
0.316
0.621
0.185
0.368
0.160
0.304
0.187
0.294
-0.129***
-0.327***
2009
ST
LT
0.240
0.384
0.338
0.603
0.213
0.362
0.196
0.286
0.212
0.283
-0.126***
-0.319***
Mean 2001–2005 (1)
ST
LT
0.175
0.891
0.281
0.909
0.148
0.890
0.126
0.883
0.144
0.880
Mean 2006–2009 (2)
ST
LT
0.199
0.418
0.302
0.642
0.175
0.392
0.151
0.322
0.168
0.316
Diff. (2)–(1)
ST
LT
0.024***
-0.473***
0.021***
-0.267***
37
0.026***
-0.499***
0.025***
-0.561***
0.024***
-0.564***
0.003
-0.298***
Table 4
Effect of the Reform on Leverage: DID Regressions
This table shows the results from the following regression:
Yi,t = α + αi + αt + βP ostt × T reatedi + Xi,t γ + εi,t ,
where Yi,t is either total, short-term, or long-term leverage or dummy variables indicating whether the former
quantities are zero. Total (short-term) [long-term] leverage is the sum of short-term and long-term (short-term)
[long-term] debt scaled by total assets. Dummy no total (short-term) [long-term] leverage is a dummy variable
equal to one if total (short-term) [long-term] leverage is zero and else zero. Post is a dummy variable equal to
one for years greater or equal to 2006 and else zero. Treated is a dummy variable equal to one for firms whose
mean fixed asset-to-total asset ratio is in the top quartile and else zero. Xi,t is a vector of control variables,
including size, profitability, and age. Size is the log of total assets; Profitability is the ratio of earnings before
interest and taxes to total assets; and Age is the log of the current year minus the year of incorporation. α,
β, and γ are free parameters. αi and αt indicate firm- and year-fixed effects. εi,t is the residual. Parameter
estimates are in normal letter, while t-statistics are in square parentheses. T-statistics are calculated from
standard errors that are clustered at the firm-level. ∗∗∗ , ∗∗ , and ∗ indicate statistical significance at the 99%,
95%, and 90% confidence levels, respectively. The sample period is 2001 to 2009.
Dependent Variable
Intensive Margin
Post × Treated
Size
Profitability
Age
R-squared
Observations
Extensive Margin
Total
Leverage
Short-term
Leverage
Long-term
Leverage
Dummy
No Total
Leverage
0.040***
[22.43]
0.021***
[16.40]
-0.189***
[-36.33]
-0.002
[-1.02]
-0.002*
[-1.77]
0.013***
[13.53]
-0.127***
[-32.79]
0.001
[0.63]
0.044***
[32.45]
0.008***
[11.89]
-0.050***
[-16.39]
-0.003**
[-2.36]
0.004
[1.27]
-0.071***
[-27.23]
0.077***
[7.14]
-0.034***
[-7.14]
0.006
[1.60]
-0.077***
[-26.87]
0.095***
[7.59]
-0.044***
[-8.00]
-0.119***
[-19.07]
-0.047***
[-15.51]
0.104***
[7.41]
0.079***
[11.80]
0.040
229,298
0.016
229,298
0.087
229,298
0.010
229,298
0.007
229,298
0.232
229,298
38
Dummy No
Short-term
Leverage
Dummy No
Long-term
Leverage
Table 5
Effect of the Reform on Loan Contracting Terms: DID Regressions
This table shows the results from the following regression:
Zi,j,t = α + αk + αt + βP ostt × T reatedi,j,t + Xi,t γ + Wi,j,t δ + εi,j,t ,
where Zi,j,t is either the loan spread, defined as the log of the sum of a loan’s coupon and annual fees scaled by
its nominal value minus the six month LIBOR rate (in basis points), or the loan time-to-maturity, defined as
the log of the difference between the loan’s maturity date and its initiation date (in months). Post is a dummy
variable equal to one for years greater or equal to 2006 and else zero. Treated is a dummy variable equal to
one for secured loans and else zero. Xi,t is a vector of firm-specific control variables. Wi,j,t is a vector of loan
contract-specific control variables. Size is the log of total assets; Age is the log of the current year minus the year
of incorporation; Profitability is the ratio of earnings before interest and taxes to total assets; and TotalLeverage
is the ratio of the sum of short-term and long-term debt to total assets. Rating is a dummy variable equal to
one if the firm taking out the loan is rated, else zero. CreditSpread is the mean monthly difference between
the yields of a corporate bond index and a long-term government bond index, and TermSpread that between
the yield of a long-term and a short-term government bond index, where means are calculated over the current
year. LoanSize is the log of the notional value of the loan; and LoanType is a dummy variable equal to one for
term loans, else zero. We also add the other endogenous variable to the control variables. α, β, δ, and γ are
free parameters. αk and αt indicate industry- and year-fixed effects. Industry effects are based on 2-digit SIC
codes. εi,t is the residual. Parameter estimates are in normal letter, while t-statistics are in square parentheses.
T-statistics are calculated from standard errors clustered at the industry-level. ∗∗∗ , ∗∗ , and ∗ indicate statistical
significance at the 99%, 95%, and 90% confidence levels, respectively. The sample period is 2001 to 2009.
Dependent Variable
Loan Spread
Post × Treated
-1.291***
[-4.09]
-1.257***
[-5.16]
0.032
[1.07]
-0.031
[-1.24]
0.265
[0.67]
-0.065
[-0.43]
-0.035
[-0.15]
1.086***
[11.70]
0.183***
[4.29]
-0.080***
[-5.25]
Size
Age
Profitability
TotalLeverage
Rating
CreditSpread
TermSpread
LoanAmount
Loan Maturity
1.078***
[5.58]
0.195***
[5.98]
-0.063***
[-2.87]
0.786***
[6.27]
-0.298***
[-3.44]
-0.079***
[-3.65]
-0.020
[-0.96]
0.871***
[6.94]
-0.023
[-1.47]
0.033***
[3.83]
-0.082
[-0.38]
0.100
[0.75]
0.168**
[1.99]
-0.388***
[-7.02]
-0.066***
[-2.73]
0.001
[0.06]
(continued on next page)
39
Table 5
Effect of the Reform on Loan Contracting Terms: DID Regressions (continued)
Dependent Variable
Loan Spread
LoanType
LoanMaturity
0.304***
[11.51]
0.249***
[5.17]
Loan Maturity
0.314***
[10.77]
0.333***
[6.05]
LoanSpread
Treated
R-squared
Observations
1.550***
[16.77]
1.537***
[8.58]
0.445
456
0.473
409
40
0.032
[1.07]
0.201***
[3.64]
-0.422***
[-3.69]
0.329
456
0.023
[0.74]
0.236***
[4.88]
-0.576***
[-3.15]
0.410
409
41
Assets (in million $)
Firm Age (in years)
Number of Employees
Operating Profit
Operating Profit Volatility
Cash Reserve
Capital-to-Labor
Tangible Asssets
Fixed Assets
Distance to Top 5 City (in miles)
Distance to Top 10 City (in miles)
Distance to Capital Market (in miles)
26.160
22.752
132.330
0.105
0.044
0.121
2.481
0.131
0.228
76.394
42.692
165.103
All Companies
without Long-term
Debt Before 2006
(1)
25.121
23.929
142.752
0.111
0.039
0.107
2.734
0.165
0.270
89.711
51.036
190.631
Companies Without Longterm Debt Before 2006 but
Positive Long-term Debt in
All Post-Reform Years
(“Debt Switchers”)
(2)
28.956
22.084
123.199
0.097
0.050
0.137
2.161
0.095
0.179
58.066
31.814
128.040
Companies Without Longterm Debt Before 2006 and
No Long-term Debt in All
Post-Reform Years
(“Non-Switchers”)
(3)
-3.835
1.845
19.553
0.015
-0.012
-0.030
0.574
0.070
0.091
31.645
19.222
62.591
Difference
(2)-(3)
(0.00)
(0.00)
(0.27)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
p-value
Difference
(2) - (3)
Table 6
Comparison of Debt Switchers and Non-Switchers
This table compares several firm characteristics across firms that strongly benefitted from the reform and those that did not. The table only considers
firms that never had any long-term debt before the reform (column (2)). From the firms in column (2), the reform beneficiaries are then those firms
that had a positive long-term leverage in every year after the reform (column (3)), while the non-beneficiaries are those firms that continued to never
have any long-term debt after the reform (column (4)). The difference in firm characteristics across beneficiaries and non-beneficiaries is given in
column (5) and its p-value in column (6). The p-value is based on standard errors assuming unequal variances. To calculate the table entries, we first
average a variable’s pre-reform values by firm and then by group. Assets is total assets in million $; Firm Age is the current year minus the year of
incorporation; and Employees is the number of employees. Profitability is the ratio of earnings before interest and taxes to total assets; Profit volatility
is the standard deviation of profitability over the most recent four fiscal years, including the most recent one. We set profit volatility equal to missing
if it is based on fewer than three observations. Cash is cash reserves divided by total assets. The capital-to-labor ratio is the log of the ratio of tangible
fixed assets to the number of employees; tangibility is the ratio of tangible fixed assets to total assets; and fixed assets is the ratio of fixed assets to
total assets. We calculate the three distance variables by applying the formula for the spherical distance between two points to the longitudes and
latitudes associated with a firm’s postcode and those associated with the city centers of the five/ten biggest French cities or the Paris Bourse.
Table 7
Effect of the Reform on the Leverage of Start-Ups: ANOVA Tests
This table shows the long-term leverage of start-ups in their year of incorporation by year and fixed asset quartile
over the 2001–2009 period. We examine two attributes of long-term leverage in the table: the average (in Panel A)
and the proportion of zero long-term leverage firms (in Panel B). Long-term leverage (“LongTermLeverage”) is
defined as long-term debt scaled by total assets. Fixed assets (“FixedAssets”) is defined as fixed assets scaled
by total assets. We sort firms into fixed asset quartiles according to the values of fixed assets and the fixed asset
quartile breakpoints in the current year. Quartile Q1 contains firms with a low-fixed asset ratio, while Quartile
Q4 contains firms with a high ratio. At the bottom of the table, we report averages for the pre-reform period
attributes (2001–2005) and averages for the post-reform period attributes (2006–2009), and their differences.
The final column shows the differences in attributes across the highest (Q4) and lowest (Q1) fixed asset quartile.
∗∗∗ ∗∗
, , and ∗ indicate statistical significance at the 99%, 95%, and 90% confidence levels, respectively, with
standard errors clustered at the firm level (whenever possible).
Fixed Asset Quartile
Year
Q1
Q2
Q3
Q4
Q4–Q1
Panel A: Long-term Leverage
2001
0.023
0.006
0.014
0.021
0.050
0.043**
2002
0.019
0.000
0.001
0.015
0.061
0.061***
2003
0.031
0.000
0.027
0.025
0.073
0.073***
2004
0.027
0.004
0.027
0.041
0.039
0.036**
2005
0.036
0.018
0.022
0.023
0.081
0.063*
2006
0.048
0.015
0.029
0.040
0.111
0.096***
2007
0.053
0.022
0.018
0.055
0.119
0.098***
2008
0.057
0.017
0.025
0.073
0.116
0.099***
2009
0.072
0.010
0.022
0.116
0.146
0.136***
Mean 2001–2005 (1)
Mean 2006–2009 (2)
0.027
0.058
0.006
0.016
0.018
0.023
0.025
0.071
0.061
0.123
Diff. (2)–(1)
0.031***
0.010
0.005
0.046***
0.062***
Panel B: Proportion of Zero Long-term Leverage Firms
2001
0.903
0.926
0.907
0.907
0.870
-0.056
2002
0.931
1.000
0.952
0.935
0.839
-0.161***
2003
0.886
1.000
0.873
0.813
0.857
-0.143***
2004
0.892
0.963
0.927
0.866
0.806
-0.157***
2005
0.904
0.981
0.900
0.936
0.783
-0.199***
2006
0.751
0.954
0.780
0.561
0.684
-0.270***
2007
0.742
0.938
0.877
0.644
0.500
-0.438***
2008
0.714
0.918
0.821
0.482
0.607
-0.311***
2009
0.741
0.953
0.778
0.600
0.588
-0.365***
Mean 2001–2005 (1)
Mean 2006–2009 (2)
0.903
0.737
0.974
0.941
0.912
0.814
0.891
0.572
0.831
0.595
Diff. (2)–(1)
All
Diff.
-0.166***
-0.033**
-0.098***
42
-0.320***
-0.236***
0.052**
-0.203***
43
0.026
0.086
0.060
0.049***
Mean 2001–2005 (1)
Mean 2006–2009 (2)
Diff. (2)–(1)
Mean 2001–2005 (3)
Mean 2006–2009 (4)
Diff. (3)–(4)
Diff. [(3)–(4)]–[(1)–(2)]
Q4
0.009
0.020
0.011
Q1
0.100***
0.023
0.128
0.105
0.005
0.011
0.005
Q4
Land Index
Quartiles
Q1
Quartile
Fixed
Asset
0.051***
-0.002
0.042***
0.045***
-0.003***
-0.009***
-0.006***
Q4–Q1
Diff.
0.051***
0.027
0.093
0.066
0.008
0.022
0.015
Q1
0.091***
0.023
0.123
0.100
0.010
0.018
0.009
Q4
Building Index
Quartiles
0.040***
-0.004**
0.030***
0.034***
0.002
-0.004**
-0.006***
Q4–Q1
Diff.
0.044***
0.023
0.081
0.058
0.008
0.021
0.014
Q1
0.056***
0.025
0.092
0.067
0.011
0.022
0.011
Q4
Machinery
Index Quartiles
0.012**
0.002
0.011***
0.009**
0.004***
0.001
-0.003
Q4–Q1
Diff.
Table 8
Effect of the Reform on Firms with Different Fixed Asset Types: ANOVA Tests
This table shows mean long-term leverage by fixed asset quartile and one of three fixed asset-type indexes for the 2001–2005 period and the 2006–2009
period. The fixed asset-type indexes are a land, a building, and a machinery index. Long-term (“LongtermLeverage”) is defined as long-term debt
scaled by total assets. Fixed assets (“FixedAssets”) is defined as fixed assets scaled by total assets. The LandIndex is the ratio of land-to-total assets,
the BuildingIndex index the ratio of buildings-to-total assets, and the MachineryIndex the ratio of machinery and equipment-to-total assets, first
averaged by 4-digit SIC code industry and month and then by 4-digit SIC code industry. The fixed asset-type indexes are constructed from global
COMPUSTAT and WorldScope data. We double-sort firms into quartiles according to their fixed assets and fixed asset-type index values and the
fixed asset and fixed asset-type index quartile breakpoints in the current year. Quartile Q1 contains firms with a low sorting variable value, while
Quartile Q4 contains firms with a high sorting variable value. For the sake of brevity, the table only shows results for the highest (Q4) and lowest
(Q1) quartiles. The table reports the time-series average of the annual numbers for the pre-reform period (2001–2005) and the post-reform (2006–2009)
period, and their differences (Diff). Conditioning on the fixed asset (fixed asset-type index) quartile, it also reports the difference in the change in
long-term leverage across the highest and lowest fixed asset-type index (fixed asset) quartile. ∗∗∗ , ∗∗ , and ∗ indicate statistical significance at the 99%,
95%, and 90% confidence levels, respectively, based on standard errors clustered at the firm level.
44
+γP ostt × HighCourtEf f iciencyk + δP ostt × T reatedi + Xi,t η + εi,t ,
= α + αi + αt + βP ostt × T reatedi × HighCourtEf f iciencyk
4.01%
227,428
R-squared
Observations
Age
Profitability
Size
Post × Treated
Post × HighCourtEfficiency
0.000
[-0.01]
0.000
[-0.05]
0.040***
[15.90]
0.021***
[16.27]
-0.189***
[-36.20]
-0.002
[-0.89]
Post × Treated × HighCourtEfficiency
Total
Leverage
1.62%
227,428
0.002
[0.88]
0.002**
[2.26]
-0.003*
[-1.89]
0.013***
[13.52]
-0.128***
[-32.88]
0.001
[0.78]
Short-term
Leverage
Intensive Margin
8.76%
227,428
-0.001
[-0.50]
-0.003***
[-3.39]
0.044***
[23.01]
0.008***
[11.73]
-0.050***
[-16.15]
-0.003**
[-2.34]
Long-term
Leverage
0.94%
227,428
0.64%
227,428
0.004
[0.54]
-0.001
[-0.18]
0.004
[0.83]
-0.077***
[-26.78]
0.096***
[7.64]
-0.044***
[-7.95]
Dummy
Short-term
Leverage
Extensive Margin
0.003
[0.43]
0.001
[0.16]
0.003
[0.66]
-0.071***
[-27.06]
0.076***
[7.06]
-0.034***
[-7.13]
Dummy
Total
Leverage
Dependent Variable
23.51%
227,428
-0.034***
[-2.77]
0.037***
[5.85]
-0.100***
[-11.45]
-0.046***
[-15.23]
0.102***
[7.26]
0.078***
[11.59]
Dummy
Long-term
Leverage
where Yi,t is total, short-term, or long-term leverage or dummy variables indicating whether the former quantities are zero. Total (short-term) [longterm] leverage is the sum of short-term and long-term (short-term) [long-term] debt scaled by total assets. Dummy no total (short-term) [long-term]
leverage is a dummy variable equal to one if total (short-term) [long-term] leverage is zero and else zero. Post is a dummy variable equal to one for
years greater or equal to 2006 and else zero. Treated is a dummy variable equal to one for firms whose mean fixed asset-to-total asset ratio is in the
top quartile and else zero. HighCourtEfficiency is a dummy variable equal to one for firms located in Departments for which the average length of
insolvency proceedings is below the sample median and else zero. Xi,t is a vector of control variables, including size, profitability, and age. Size is the
log of total assets; Profitability is the ratio of earnings before interest and taxes to total assets; and Age is the log of the current year minus the year
of incorporation. α, β, γ, δ, and η are free parameters. αi and αt indicate firm- and year-fixed effects. εi,t is the residual. Parameter estimates are in
normal letter, while t-statistics are in square parentheses. T-statistics are calculated from standard errors that are clustered at the firm-level. ∗∗∗ , ∗∗ ,
and ∗ indicate statistical significance at the 99%, 95%, and 90% confidence levels, respectively. The sample period is 2001 to 2009.
Yi,t
Table 9
Intermediating Effect of Court Efficiency on the Reform’s Financing Results: DIDID Regressions
This table shows the results from the following regression:
Table 10
Effect of the Reform on Firm Performance: DID Regressions
This table shows the results from the following regression:
Yi,t = α + αi + αt + βP ostt × T reatedi + Xi,t γ + εi,t ,
where Yi,t is growth, employees, sales, profitability, or profit volatility. Growth is the sum of the change in
intangible fixed assets and the change in inventories, where both changes are calculated from the prior fiscal
year end to the current one and the sum is scaled by the average of total assets over the two fiscal year ends.
Employees is the log of the number of employees. Sales is the log of sales. Profitability is earnings before interest
and taxes scaled by total assets. ProfitVolatility is the standard deviation of Profitability over the most recent
four fiscal years, including the most recent one. We set ProfitVolatility equal to missing if it is based on fewer
than three observations. Post is a dummy variable equal to one for years greater or equal to 2006 and else zero.
Treated is a dummy variable equal to one for firms whose mean fixed asset-to-total asset ratio is in the top
quartile and else zero. Xi,t is a vector of control variables, including size, total leverage, and age. Size is the log
of total assets; TotalLeverage is total debt scaled by total assets; and Age is the log of the current year minus the
year of incorporation. α, β, and γ are free parameters. αi and αt indicate firm- and year-fixed effects. εi,t is the
residual. Parameter estimates are in normal letter, while t-statistics are in square parentheses. T-statistics are
calculated from standard errors that are clustered at the firm-level. ∗∗∗ , ∗∗ , and ∗ indicate statistical significance
at the 99%, 95%, and 90% confidence levels, respectively. The sample period is 2001 to 2009.
Dependent Variable
Growth
Post × Treated
Size
TotalLeverage
Age
R-squared
Observations
0.006***
[6.90]
0.045***
[49.11]
-0.021***
[-7.57]
-0.045***
[-21.99]
0.014
209,979
Employees
0.051***
[7.05]
0.503***
[59.58]
-0.010
[-0.63]
0.171***
[18.61]
0.450
174,872
45
Sales
Profitability
Profit
Volatility
0.041***
[5.69]
0.780***
[102.87]
-0.223***
[-13.60]
0.211***
[24.59]
0.005***
[4.39]
0.012***
[12.54]
-0.106***
[-36.50]
0.021***
[13.95]
-0.002***
[-4.08]
-0.010***
[-16.98]
0.012***
[8.26]
-0.018***
[-10.79]
0.597
231,112
0.004
229,298
0.035
158,374
Table 11
Effect of the Reform on Failure Rates: DID Regressions
This table shows the results from the following proportional Cox hazard model:
λi,t = φt exp (βP ostt × T reatedi + Xi,t γ) ,
where the hazard rate, λi,t , is the probability of firm i failing at time t conditional on surviving until then,
and φt is the “baseline” hazard rate common to all firms. We set a firm’s failure year to the calendar year
in which its legal status changes from an active status to one of the failure statuses: “default of payment”,
“insolvency proceedings,” “receivership,” “bankruptcy,” “dissolved (bankruptcy),” “dissolved (liquidation),” and
“in liquidation.” We exclude a firm from the analysis if it is unclear whether it became inactive for performance
reasons, that is, when legal status changes to: “inactive (no precision),” “unknown,” and “dissolved.” Post is a
dummy variable equal to one for years greater or equal to 2006 and else zero. Treated is a dummy variable equal
to one for firms whose mean fixed asset-to-total asset ratio is in the top quartile and else zero. Xi,t is a vector
of control variables, including total leverage, profitability, profit volatility, size, and cash reserves. TotalLeverage
is the sum of short-term and long-term debt to total assets. Profitability is the ratio of earnings before interest
and taxes to total assets. ProfitVolatility is the standard deviation of Profitability over the most recent four
fiscal years. We set ProfitVolatility equal to missing if it is based on fewer than three observations. Size is the
log of total assets, and Cash is cash reserves over total assets. β and γ are free parameters. Parameter estimates
are in normal letter, while t-statistics are in square parentheses. T-statistics are calculated from standard errors
that are clustered at the firm-level. ∗∗∗ , ∗∗ , and ∗ indicate statistical significance at the 99%, 95%, and 90%
confidence levels, respectively. The sample period is 2001 to 2009.
Dependent Variable = Time-to-Failure
Post × Treated
-0.338***
[-4.36]
-0.391***
[-5.01]
-0.458***
[-5.74]
0.821***
[4.84]
-0.112***
[-4.89]
-3.497***
[-10.71]
-0.108***
[-4.75]
-3.264***
[-9.92]
TotalLeverage
Profitability
ProfitVolatility
Size
Cash
R-squared
Observations
0.001
182,249
0.008
177,904
46
0.009
177,904
-0.206**
[-2.13]
0.295
[1.61]
-4.612***
[-12.09]
2.630***
[3.09]
-0.090***
[-3.22]
-2.690***
[-6.48]
0.037
127,767
Table 12
Effect of the Reform on Capital Allocation
This table shows the results from the following panel-data regression:
InvestmentGrowthk,t = α + βV alueAddedGrowthk,t + εk,t ,
where InvestmentGrowthk,t is gross fixed capital formation for industry k in year t, and V aluedAddedGrowthk,t
is the value-added. We perform this regression using pre-reform (2001–2005) and post-reform (2006–2007) data.
α and β are free parameters, and εk,t is the residual. In addition to the parameter estimates and their inference
levels (T-Statistic), the table shows the number of observations (Obs) and the R-squared (R-squared) per
regression, and it also reports the difference in the slope coefficient across the two periods.
Year
Obs
Elasticity (β)
T-Statistic
R-Squared
2001-2005 (1)
2006-2007 (2)
566
237
0.434
0.697
6.15
5.34
0.062
0.104
0.263
1.79
Diff (2)–(1)
47
48
All (1)
0.016
0.015
0.015
0.014
0.016
0.051
0.057
0.069
0.079
0.015
0.063
0.048***
Year
2001
2002
2003
2004
2005
2006
2007
2008
2009
Mean 2001–2005 (1)
Mean 2006–2009 (2)
Diff (2)–(1)
0.082***
0.023
0.104
0.023
0.022
0.021
0.022
0.026
0.085
0.095
0.115
0.125
Treated (2)
High
0.030***
0.012
0.042
0.013
0.012
0.011
0.010
0.012
0.033
0.037
0.045
0.054
Controls (3)
0.029***
0.015
0.044
0.017
0.015
0.014
0.013
0.014
0.037
0.039
0.048
0.053
All (4)
Low
0.053***
0.024
0.077
0.027
0.022
0.024
0.023
0.024
0.065
0.071
0.082
0.091
Treated (5)
External Financial Dependence
0.025***
0.013
0.038
0.015
0.014
0.013
0.011
0.012
0.031
0.033
0.042
0.046
Controls (6)
0.019***
-0.001
0.000
0.000
0.001
0.003***
0.014***
0.018***
0.021***
0.026***
All (1)–(4)
0.029***
-0.004*
0.000
-0.003
-0.002
0.001
0.020***
0.024***
0.033***
0.034***
Treated (2)–(5)
Dependence Across
High–Low External Financial
Table 13
Effect of the Reform on Firms with a High and Low External Financial Dependence: ANOVA Tests
This table shows the mean long-term leverage of all, high fixed asset-, and low fixed asset-firms with a high or low dependence on external financing, by year
and over the 2001–2009 period. Long-term leverage (“TotalLeverage”) is defined as long-term debt scaled by total assets. Fixed assets (“FixedAssets”)
is defined as fixed assets scaled by total assets. We obtain an industry-specific measure of external finance dependence (“ExternalFinancialDependence”)
by calculating the median proportion of capital expenditure that are not financed by cash flows from operations for U.S. public firms. We perform
these calculations using COMPUSTAT data over the period from 1975–2005. We classify as firms with a high (low) external financial dependence
those operating in industries with an above (below) median ExternalFinancialDependence. The treated firms are those with a mean fixed asset-to-total
asset ratio in the top quartile, while the control firms are those in the remaining three quartiles. At the bottom of the table, we report averages for
the pre-reform period means (2001–2005) and averages for the post-reform period means (2006–2009), and their differences. The penultimate (final)
column shows the differences in means across all firms (treated firms) with a high and those with a low external financial dependence. ∗∗∗ , ∗∗ , and ∗
indicate statistical significance at the 99%, 95%, and 90% confidence levels, respectively, based on standard errors clustered at the firm level.
Table 14
Migration Rates Between the High and Low Fixed Asset Groups
This table shows migration rates between the high- and the low-fixed asset group over various five year-periods
in the 1996–2009 period. The migration rates are calculated using the first and the last year in the five year
period. A firm belongs to the high-fixed asset group if its fixed asset-to-total asset ratio is in the top quartile
in a given year. Otherwise, it belongs to the low-fixed asset group.
Period
Moved Into
the High-Fixed
Asset Group
Stayed in the
Same Group
Moved Into
the Low-Fixed
Asset Group
1996-2000
0.077
0.852
0.071
1997-2001
0.084
0.845
0.071
1998-2002
0.078
0.853
0.069
1999-2003
0.073
0.857
0.070
2000-2004
0.075
0.860
0.065
2001-2005
0.068
0.862
0.070
2002-2006
0.065
0.867
0.068
2003-2007
0.066
0.869
0.065
2004-2008
0.064
0.866
0.070
2005-2009
0.060
0.869
0.071
49
Table 15
Effect of the Reform on the Long-term Leverage of Public Firms: ANOVA Tests
This table shows the mean long-term leverage of public firms by year and fixed asset quartile over the 2001–2009
period. Long-term leverage (“LongTermLeverage”) is defined as long-term debt scaled by total assets. Fixed
assets (“FixedAssets”) is defined as fixed assets scaled by total assets. We sort firms into fixed asset quartiles
according to the values of fixed assets and the fixed asset quartile breakpoints in the current year. Quartile Q1
contains firms with a low-fixed asset ratio, while Quartile Q4 contains firms with a high ratio. At the bottom
of the table, we report averages for the pre-reform period means (2001–2005) and averages for the post-reform
period means (2006–2009), and their differences. The final column shows the differences in means across the
highest (Q4) and lowest (Q1) fixed asset quartile. ∗∗∗ , ∗∗ , and ∗ indicate statistical significance at the 99%, 95%,
and 90% confidence levels, respectively, based on standard errors clustered at the firm level.
Fixed Asset Quartile
Diff.
Year
All
Q1
Q2
Q3
Q4
Q4–Q1
2001
0.092
0.047
0.054
0.089
0.179
0.132***
2002
0.106
0.059
0.073
0.116
0.177
0.119***
2003
0.098
0.051
0.057
0.095
0.186
0.135***
2004
0.097
0.045
0.058
0.104
0.183
0.138***
2005
0.085
0.029
0.064
0.091
0.156
0.128***
2006
0.102
0.047
0.078
0.121
0.162
0.116***
2007
0.112
0.093
0.074
0.118
0.162
0.069*
2008
0.134
0.144
0.070
0.151
0.171
0.027
2009
0.118
0.069
0.096
0.132
0.174
0.105***
Mean 2001–2005 (1)
Mean 2006–2009 (2)
0.096
0.116
0.046
0.088
0.061
0.080
0.099
0.131
0.176
0.167
Diff. (2)–(1)
0.021**
0.042
0.019
0.032
-0.009
50
-0.051*