Unconventional Monetary Policy and SME Expectations of
Future Credit Availability
Annalisa Ferrando
ECB

Alexander Popovy
ECB

Gregory F. Udell
Indiana University

Abstract
We study the impact of the announcement of the ECB’s Outright Monetary Transactions
Program on small …rms’expectations about the availability of future …nancing through credit
markets. We …nd that following the announcement, …rms’ expectations improved throughout
the Euro Area. Importantly, expectations improved signi…cantly more for …rms borrowing frombanks with high balance sheet exposures to impaired sovereign debt. We also …nd that …rms
expecting the availability of bank credit to improve in the future invest more and engage inmore product innovation, suggesting that unconventional monetary policy can stimulate thereal economy through a credit-access-expectations channel.
JEL classi…cation: D22, E58, G21, H63.
Keywords: Unconventional monetary policy, sovereign stress, …rm expectations.

The opinions expressed herein are those of the authors and do not necessarily re‡ect those of the ECB or theEurosystem.
yCorresponding author. European Central Bank, Financial Research Division, Sonnemannstrasse 20, D-60314
Frankfurt, email: Alexander.Popov@ecb.int

1Unconventional Monetary Policy and SME Expectations of
Future Credit Availability

Abstract
We study the impact of the announcement of the ECB’s Outright Monetary Transactions
Program on small …rms’expectations about the availability of future …nancing through credit
markets. We …nd that following the announcement, …rms’ expectations improved throughout
the Euro Area. Importantly, expectations improved signi…cantly more for …rms borrowing frombanks with high balance sheet exposures to impaired sovereign debt. We also …nd that …rms
expecting the availability of bank credit to improve in the future invest more and engage inmore product innovation, suggesting that unconventional monetary policy can stimulate thereal economy through a credit-access-expectations channel.
JEL classi…cation: D22, E58, G21, H63.
Keywords: Unconventional monetary policy, sovereign stress, …rm expectations.

1Introduction

On July 25, 2012, at the peak of the euro-area sovereign debt crisis, the President of the European
Central Bank (ECB) Mario Draghi delivered a speech in London which culminated in the following
statement: "Within its mandate, the ECB is ready to do whatever it takes to preserve the euro.
And believe me, it will be enough." These much quoted two short sentences are possibly the best
example of successful monetary policy communication during the 20-year history of the ECB.
Draghi’s speech contained no details beyond the promise to act, and it took another month toannounce the Outright Monetary Transactions (OMT) Program, which ‡eshed out the ECB’s
commitment to preserve the euro by addressing bond and …nancial market disfunctionality.1 Yet,markets were immediately relieved, and bond yields on sovereign debt issued by …scally stressedcountries declined immediately, sharply, and permanently.2 The sovereign debt crisis that had
raged for more than two years, threatening the future of the common currency itself, essentiallyended on that day.
While recent evidence has suggested that unconventional monetary policy can a¤ect the real
economy through the bank lending channel, e.g. by reducing the funding costs of banks (Acharya
et al., 2019; Ferrando et al., 2019), the above narrative suggests that much of its impact can comevia the channel of expectations. One manifestation of this channel is related to …rms’beliefs thatfunding conditions will improve in the future, whereby improvements in expectations of future credit
availability can a¤ect …rms’investment decisions even in the absence of an actual improvement incredit conditions. In this paper, we use a novel micro-level dataset to study the impact of the
OMT announcement on …rms’ expectations of future credit availability, and the transmission ofthese changes in expectations to …rms’ real decisions. This line of analysis is important for two
reasons. First, while shocks to in‡ation expectations have been studied extensively (e.g., Cogley andSargant, 2008; Orphanides and Williams, 2008; Guiliano and Spilimbergo, 2014), to our knowledge
we are the …rst to analyze the interplay among monetary policy, …rms’ expectations of future
funding, …rms’ real decisions, and the conditions of …rms’ banks. Because …rm investment andproductivity-enhancing activities are central to many macroeconomic models, it is important to1

Under the OMT Program, the ECB committed itself— under strict conditionality— to purchasing unlimited
amounts of eligible sovereign bonds, making it the largest and the most ambitious unconventional monetary policy ever implemented in the euro area.
2
See Altavilla et al. (2016).

1

understand the mechanisms driving this interplay. Second, because the past decade has seen a wide
range of unconventional monetary policies implemented by central banks throughout the world, andbecause unconventional policies seem to have entered permanently the toolbox of modern monetary
authorities, the question we study in this paper has external validity which goes beyond one speci…c
episode.
How should we expect unconventional monetary policy to a¤ect …rms’beliefs about future credit
market conditions? In answering this question, we are guided by Gertler and Karadi (2011) whointerpret unconventional monetary policy in terms of expanding central bank credit intermediationto o¤set a disruption of private …nancial intermediation.3 Their theoretical model incorporates…nancial intermediaries into a standard macroeconomic model and shows how the ‡ow of credit isin‡uenced by the condition of bank balance sheets, given an agency problem between intermediaries
and depositors that drives an endogenous constraint on the intermediary’s leverage ratio. In theirmodel the Central Bank can intervene in a …nancial crisis using unconventional monetary policy
because it does not face an agency problem-driven constraint on its leverage ratio. This type offramework is not only useful in thinking about how the OMT— as an extreme example of unconventional monetary policy— can e¤ect contemporaneous …rm access to credit, but also how it cane¤ect expectations about future …rm access to …nance. We focus on the latter e¤ect by analyzing
how the ECB’s announcement of the OMT a¤ected euro area SMEs’expectation of future accessto credit using survey data on euro area …rms.
We present four main sets of …ndings. First, the announcement of the OMT Program had asigni…cant e¤ect on expectations about future credit availability for all …rms in the euro area. In
the year after the announcement, the average euro-area …rm was 20-percent more likely to expectthat …nancing through banks will improve in the short-to-medium term, compared to similar …rms
during the year before the announcement. Second, this e¤ect was particularly strong for …rms
borrowing from banks with signi…cant balance sheet exposures to impaired sovereign debt. Thissuggests the existence of a funding-expectations channel of unconventional monetary policy thatworks through bank balance sheet strength. Third, …rms with higher expectations of future credit
availability have higher investment and are more likely to engage in innovation. This e¤ect is
3This was certainly the case with the OMT. In a subsequent speech on September 6, 2012, outlining the detailsof the OMT, Draghi was explicit: "Furthermore, in a number of euro area countries, the segmentation of …nancial
markets and capital constraints for banks continue to weigh on credit supply."2

observed while controlling for current credit conditions, suggesting that the funding-expectations
channel can impact …rms’real decision over and above …rms’actual access to …nance. Finally, we…nd that expectations of future credit availability lead actual credit availability by 6 months to
one year, suggesting that empirical tests which fail to control for the role of …rm expectations canoverstate the impact of credit access on …rm growth.
We subject the main …ndings in the paper to a battery of sensitivity tests aimed at strengthening
the identi…cation of the causal e¤ects of unconventional monetary policy. First, we employ countrysector-time …xed e¤ects in all regressions, thereby netting out the e¤ect of shocks that are common
to all …rms in a country at the same point in time (e.g., the perception that the euro itself willsurvive), and to all …rms in the same sector in the same country at the same point in time (e.g.,
shocks to the demand for German manufacturing goods in the second half of 2012). Second, we
employ bank …xed e¤ects, thereby soaking up the e¤ect of shocks to all …rms borrowing from thesame bank at the same point in time. Third, we show that the trend in beliefs about future credit
availability do not predate the OMT announcement. Fourth, we run our tests on the subset of…rms that are observed more than once during the sample period. This allows us to include …rm…xed e¤ects in the regression which reduces concerns about omitted variable bias at the …rm level
related, for example, to unobservable investment opportunities. Finally, we show that the sametransmission channel did not a¤ect …rm expectations of future availability of …nancing via nonbank sources, such as equity, trade credit, and bond securities. Our …ndings thus suggest that wehave indeed identi…ed an expectations channel of monetary policy that works through …rms’beliefs
about the availability of bank credit in the future.
Our paper is related to a number of di¤erent literatures. First, the analysis directly relates
to the literature on monetary policy and expectations. There is, of course, a large literatureon this topic. Coibion and Gorodnichenko (2012), Coibion et al. (2018a), and Coibion et al.
(2018) provide discussions of the importance of agent expectations in macroeconomic models andparticularly the "workhorse" role of full-information rational expectations. In addition, these two
papers also address issues that arise from deviations from full-information rational expectations
with a particular focus on the value of agent survey data like the data that we use in our paper.
Our paper is also related to papers on …rm expectations and reactions to monetary policy such as
Hachem (2017) that explores how monetary policy can a¤ect the importance of relationship lending3

and, ultimately, aggregate output.
In considering the e¤ect of the OMT program our paper necessarily relates to research onmonetary policy and the bank lending channel (e.g., Bernanke and Blinder, 1988; Kashyap andStein, 1994). Our paper also relates to the general body of work on credit and conventionalmonetary policy (e.g., Gertler and Gilchrist, 1994; Jimenez et al., 2012; Massa and Zhang, 2013)and credit and unconventional monetary policy (e.g., Acharya et al., 2018; Andrade et al., 2017;
Carpinelli and Crosignani, 2018; Crosignani et al., 2017; Daetz et al., 2016; Eser and Schwaab, 2016;
Giannone et al., 2012; Gilchrist and Zakrajsek, 2013; Gilchrist et al., 2015; Foley-Fisher et al., 2016;
Garcia-de-Andoain et al., 2016; Heider et al., 2019; Krishnamurthy and Vissing-Jorgensen, 2011;
Krishnamurthy et al., 2017).
Like the papers on the bank lending channel, and on credit and conventional and unconventionalmonetary policy, we are interested in the link between funding availability and monetary policy.
However, our paper focuses on how monetary policy – and, speci…cally, unconventional monetary
policy - a¤ects expectations about future funding availability. So, our focus on expectations sharesmuch with the broad literature on expectations and monetary policy mentioned above. However,
our focus is not on expectations about in‡ation, but rather on expectations about future accessto credit. As far as we are aware, there is only one other paper that has looked at the e¤ect ofmonetary policy on expectations about future credit availability, Dunkelberg and Scott (2009) (DS).
Like our paper, DS use …rm-level survey data to analyze changes in …rm expectations – including
expectations about changes in credit availability – in response to three di¤erent monetary policy
shocks in the: "the April 2001 surprise decrease in rates"; the "April 1994 surprise increase";
and, "the unexpected rate cuts of September 2007 through January 2008". Our paper di¤ers fromDS in a number of important ways. First and most important, we look at the response to an"unconventional" monetary policy shock, as opposed to a conventional monetary policy shock. As
we noted above unconventional monetary policy initiatives are primarily directed at mitigatingproblems in bank sector to re-energize credit availability (Gertler and Karadi, 2011). As far as
we are aware, no other paper has examined the impact of any unconventional monetary policy on…rm expectations about future credit availability. Second, unlike DS, we compare the impact of the
OMT initiative on immediate credit availability with its impact on expectations about future credit
access. Third, because of our cross-data and because we can link our …rms with their banks, we
4explore how di¤erences in country-level conditions and bank-level conditions a¤ect the expectations
response to the Euro Area wide announcement of the OMT. And, unlike DS our data allow us tocontrol in our multivariate analysis for a number of time-varying …rm-level characteristics that
could a¤ect funding expectations,4 and they allow us to examine several distinct funding channels,
rather than only bank loans.
The rest of the paper is organized as follows. Section 2 presents the institutional backgroundand our research hypotheses. Section 3 summarizes the data. Section 4 discusses the empiricalstrategy. Section 5 presents the evidence on the impact of the OMT Program. Section 6 documents
the impact of …rm expectations on real decisions, such as investment and innovation, and discusses
aggregate e¤ects. Section 7 concludes.

2

Institutional design and theoretical underpinnings

2.1

The euro area sovereign debt crisis and unconventional monetary policy

The sovereign debt crisis which erupted in the euro area in 2010 sent ripples through the global
banking system and prompted interventions by governments and central banks on a scale comparable to the programs implemented during the …nancial crisis of 2008–09. Over the course of
2010–2012, yields on sovereign bonds issued by the governments of Greece, Ireland, and Portugal
reached levels which made their overall stock of debt unserviceable, with Italy and Spain facing
record costs of issuing new debt, too. On the …scal response side, the e440 billion-strong European
Financial Stability Facility (EFSF) was established by the 27 member states of the EU in May 2010
with a mandate to provide …nancial assistance to euro area states. Its committed funding was later
boosted to around e1 trillion.
On the side of monetary policy, the ECB implemented a series of non-standard monetary policy
measures. In May 2010, the ECB instituted the Security Markets Program (SMP) whereby it
began open market operations buying government and private debt securities in secondary markets,
reaching about e220 billion in February 2012, and simultaneously absorbing the same amount ofliquidity to prevent a rise in in‡ation (Eser and Schwaab, 2015). In December 2010, the ECB
4

DS employ a univariate analysis on how each variable (i.e., each of their business plan variables and the expectedloan access variable) changes pre- and post- in response to each of the three U.S. conventional monetary policy shocks
examined in the paper.

5extended e489 billion in loans to more than 500 European banks at a …xed 1 percent interest rate.
This was followed, in February 2012, by a second long-term re…nancing operation, injecting an
additional e530 billion into the banking system.
Concerned that the e¤ect of all these interventions would be short-lived, on 2nd August 2012
the ECB announced that it would undertake outright transactions in secondary sovereign bondmarkets (OMT Program), aimed at safeguarding an appropriate monetary policy transmission andthe singleness of the monetary policy. It set a number of conditions. The technical details of the
program itself were announced on 6th September 2012. First, a country seeking access to the OMTmust request …nancial assistance from the EFSF. Second, the EU and/or IMF must agree to provide…nancial assistance through the EFSF and lay out the terms of a de…cit reduction program thatthe country must abide by. Third, the applicant country must agree to the terms of the program.
At this point, the ECB can start purchasing sovereign bonds issued by the requesting country,
focusing on the shorter part of the yield curve (with maturity of 3 years or less). The ECB set no
ex ante quantitative limits on the amount of government bonds that could be purchased throughthe OMT Program. However, in order to neutralize the potential impact on the money supply, allbond purchases would be o¤set by selling other securities of equal amount. The Program would
run until the country regained market access and could once again fund itself normally in bondmarkets.
Despite the fact that no OMT Programs were ready to start at the time of the announcement,
the …nancial markets reacted immediately by pricing in a decline of both short term and long terminterest rates in all European countries previously su¤ering from elevated interest levels. By theend of 2013, even though the ECB did not purchase a single bond through the OMT Program,
capital had ‡own back into stressed countries such as Italy and Spain, and government bond yields
had tumbled, returning to pre-crisis levels (Altavilla et al., 2016).

2.2

Unconventional monetary policy, credit access, and …rm expectations

Theory has emphasized both the role of borrowers’ balance sheets (e.g., Bernanke and Gertler,
1989; Kiyotaki and Moore, 1997; Bernanke et al., 1996), whereby expansionary monetary policy
can strengthen …rms’balance sheets by increasing cash ‡ow net of interest and by raising the valueof collateralizable assets, as well as the role of lenders’balance sheets (e.g., Bernanke and Blinder,
6

1992; Kashyap et al., 1993), whereby monetary policy regulates the pool of funds available to bankdependent borrowers in the presence of reserve requirements on bank deposits. More broadly, theory
suggests that during a crisis when lenders become more balance-sheet-constrained the bene…ts ofunconventional monetary policy increase (Gertler and Karadi, 2011).
In the case of the OMT Program, we expect the main e¤ect to come through strengtheningof the balance sheets of banks holding large amounts of sovereign debt. There are at least threemechanisms at play. First and foremost, as the OMT announcement reduces yields on previously
impaired sovereign debt, investors now perceive banks with substantial balance sheet exposures to arisky sovereign as less risky and start lowering the rates they demand to keep funding them. Thereis already abundant evidence that the OMT announcement had such an e¤ect on bank borrowing
costs. For example, Acharya et al. (2015) provide evidence that U.S. money market funds became
more willing to provide unsecured funding to European banks after the OMT announcement. In
an analysis of euro area sovereign markets, money markets, and banking markets, Szczerbowicz(2015) shows that the OMT announcement not only reduced sovereign market tensions, but also
lowered long-term bank funding costs.
Second, the eligibility of sovereign bonds as collateral to secure wholesale funding increases aswell. Drechsler et al. (2016) document how collateral quality a¤ects banks’ incentives to pledge
collateral with the central bank. Finally, as yields on sovereign debt decline, the sovereign’s ability
to support the domestic banking sector increases, and this e¤ect should be stronger for banks thatwere at a higher risk before the policy’s announcement. Consequently, banks’funding costs after
the OMT should go down relatively more for banks with large balance sheet exposures to risky
sovereign debt, leading us to expect more favorable lending conditions for SMEs that have credit
relationships with such banks.
Even though in practice we cannot distinguish among these three mechanisms, they all go in thesame direction, comprising a "bank funding" channel of unconventional monetary policy. We notethat this is a distinctly di¤erent mechanism from other channels activated by the OMT which a¤ect
all …rms in the economy equally, such as expectations about the survival of the euro or improved
consumer con…dence.
Importantly, models such as Gertler and Karadi (2011) indicate that through the bank funding
channel, unconventional monetary policy can a¤ect …rms’ actual access to …nance. A natural
7

extension of these models is that unconventional monetary policy also a¤ects expectations about
future …rm access to …nance. Because …rms know the net worth of their banks, and because
they understand that unconventional monetary policy is targeting precisely this net worth, theyrationally expect a larger improvement in future access to …nance if their main creditor bene…ts
more from unconventional monetary policy. Which of the two channels is more potent and which
one comes …rst is ultimately an empirical question. This general insight, however, allows us toformulate the following research hypothesis:
H0: The announcement of the OMT Program will improve …rm expectations about future credit
availability, more so for …rms borrowing from banks with large holdings of impaired sovereign bonds
whose value is directly a¤ ected by the Program.

3

Data

The main data source for our analysis is the …rm-level "Survey on the Access to Finance of Enterprises" (SAFE) run jointly by the ECB and the European Commission. The SAFE has been
conducted fourteen times since 2009. The survey started after the …nancial crisis initially hit theeuro area. The survey waves include the period before the sovereign debt crisis (survey waves 1
and 2, from 1st January until 31st December, 2009); the period during which the sovereign debt
crisis unfolded (wave 3, from 1st April until 30th September, 2010); the period of the sovereign
debt crisis (waves 4, 5, 6, and 7, from 1st October 2010 until 30th September 2012); and theperiod after the OMT Program announcement (waves 8 and on, from 1st October 2012). This…rm-level SME survey contains information on each respondent …rm’s characteristics (size, sector,
autonomy, turnover, age, and ownership) and on its assessment of recent short-term developments
regarding its …nancing including information on its …nancing needs and its access to …nance.5 The
sample contains only non-…nancial …rms and excludes …rms in agriculture, public administration,and …nancial services.6
Importantly, the dataset also contains information on …rms’expectations about the evolution
5

The survey’s main results are published in the ECB website every six months. For more information on thesurvey and its individual waves, see http://www.ecb.europa.eu/stats/money/surveys/sme/html/index.en.html.
6
The SAFE data include an oversample of …rms in smaller countries. For this reason, the survey providers also
compute sampling weights that adjust the sample to be representative of the frame from which the sample was drawn.
As a result, all empirical tests in the paper make use of sampling weights which restore the representativeness of eachindividual …rm with respect to the average …rm in the population of …rms in the Eurozone.

8

of the availability of future …nancing in the short-run. In particular, it includes questions about
expectations related to the availability, in the next six months, of a wide range of funding sources:
retained earnings, bank loans, bank credit lines, equity, trade credit, and debt securities. Whilea number of recent …rm-level datasets include information on …rm’s actual access to …nance andexisting mix of funding sources, the SAFE is to our knowledge the …rst …rm-level dataset thatincorporates information on …rms’ expectations about future access to …nance for the universeof funding sources. Because of the latter, the SAFE makes it possible to identify the impact oftargeted policy shocks that are expected to a¤ect …rms through well-de…ned channels.
We next merge the SAFE with Bankscope, a bank-level dataset that contains information onbanks’exposures to sovereign debt. To do so, we make use of a variable called "BANKER", madeavailable through a merge with Bureau van Dijk’s Amadeus dataset and originally acquired fromthe Kompass dataset. This variable displays the name of the banks with which the …rm has acredit relationship. Following Kalemli-Ozcan, Laeven, and Moreno (2015), we use OpenRe…ne andReconcile-CSV to match bank names to the BvD ID numbers of banks and we subsequently matchthese bank names with bank information on total sovereign bond holdings and on total assets from
Bankscope. If a …rm reports more than one bank, we use the bank reported …rst as the …rm’s mainbank. In all, we recover information on 126 banks from Bankscope in eight countries with which
the …rms in the dataset have a credit relationship.7
In our analysis, we use the four waves around the announcement of the OMT Program, waves
6 and 7 (pre-OMT) and waves 8 and 9 (post-OMT), for a total of 30,040 initial observations. Mostof the …rms are interviewed only once in the survey but there is a small subsample of …rms present
in at least two waves. In particular, out of the 21,110 unique …rms present in waves 6–9, 3,937
are observed at least once during the pre-OMT and at least once during the post-OMT period.
Once we focus only on the …rms in the dataset comprising survey waves 6, 7, 8, and 9 which report
the identity of a creditor that can be matched to Bankscope, this reduces the dataset to 2,628…rm-observations. 84 of these …rms are observed at least once during the pre-OMT and at least
once during the post-OMT period.
Table 1 reports descriptive statistics on the main variables of interest, for the sample of 2,628
7

There is no …rm-bank match for …rms in Belgium, Finland, and Italy, reducing the sample to eight countries fromthe original 11 eurozone countries in SAFE: Austria, France, Germany, Greece, Ireland, the Netherlands, Portugal,
and Spain.

9

…rms with creditor information. All survey-based percentages are weighted statistics that restore theproportions of the economic weight (in terms of number of employees) of each size class, economic
activity, and country. Bank …nancing will improve, our main dependent variable, is derived fromthe …rm’s answers to two di¤erent questions. The …rst one is: "Could you please indicate whether
the availability of bank overdrafts, credit lines or credit cards overdraft will improve, deteriorate,or remain unchanged over the next 6 months?" The second one is: The second one is: "Could
you please indicate whether the availability of bank loans (new or renewal; excluding overdraft or
credit lines) will improve, deteriorate, or remain unchanged over the next 6 months?" We construct
the variable Bank …nancing will improve as a dummy variable equal to 1 if the …rm said "Will
improve" in response to any of these two questions, and to zero if it responded "Will remainunchanged" or "Will deteriorate" to both questions. Of the 2,123 …rms that reported the identityof their creditor(s) and that gave a response to this question, 24.5 percent expect the availability of…nancing through banks to improve in the next six months. Looking at the individual componentsof Bank …nancing will improve, we …nd that 17.6 percent expect the availability of new loans toimprove, and 11 percent have such expectations about the availability of bank credit lines.8
Table 1 also reports similar summary statistics on …rms’ expectations about the availabilityof …nancing through alternative channels. The data suggest that far and beyond, bank lending is
the channel whereby most …rms expect to be able to better …nance their operations in the future.
Only 13 percent of …rms expect the availability of trade credit to increase in the next six months,
and only 9.7 percent and 11.3 percent have such expectations about equity …nancing and …nancing
through debt securities, respectively.
We also report summary statistics on the rest of the …rm-speci…c variables included in the survey
and used in our tests. On average the banks from which the …rms in our sample are borrowing holdin their portfolios sovereign bonds amounting to 5.1 percent of their total assets. Firms operating
in …scally stressed countries (Greece, Ireland, Portugal, and Spain) account for 53.4 percent of thesample, suggesting an almost even split between treatment and control …rms. 82.3 percent of the…rms are stand-alone, rather than subsidiaries of larger …rms, and 69.9 percent are individuals- orfamily-owned. By default, the survey includes mostly SMEs, with 28 percent having less than 50
8

Firms are interviewed at the end of each wave. Therefore, if a …rm is included in wave 8 (1st October 2012 –31st March 2013) and it is asked about its credit experience in the past six months, this experience is limited to theperiod 1st October 2012 – 31st March 2013 and does not spill over back into the pre-OMT period.

10

employees, and 40 percent of the …rms having more than 250 employees. In terms of turnover,around three-quarters of the …rms have an annual turnover of less than EUR 10 million. At thesame time, the …rms in the sample are relatively mature, with only 10.5 percent of them younger
than 10 years.
Turning to …rms’ credit quality and economic outlook, one-quarter of the companies in our
sample report that their outlook, in terms of sales and pro…tability, improved during the past six
months. 29.2 percent say the same about the quality of their capital, and 24.8 make a similarstatement about the quality of their credit history.
With regard to real economic activity, we use information from two sources. The SAFE contains
a question on whether …rms are currently engaged in product innovation. Using this information,
we create a dummy variable New product equal to one if the …rm answered "Yes" to the question:"In the past six months, did your …rm introduce a new or signi…cantly improved product or service
to the market?" Around a third of all …rms report that they have engaged in this type of innovation.
We also match the …rms in the SAFE to the Amadeus database, which allows us to extract
information on their investment activity. We measure …rm investment as the log di¤erence intangible assets from the pre-OMT to the post-OMT period. On average, this change is 0.5 percent.
Finally, we compare actual credit experience in the past six months and expectations of futurecredit availability. In line with Jappelli (1990), Cox and Jappelli (1993), and Duca and Rosenthal(1993), we de…ne credit constrained …rms as those who are subject to both formal and informal
credit constraints. Therefore, to us credit constrained is a …rm that declared a positive demand forbank …nancing in the past 6 months, but it was discouraged from applying because it expected tobe rejected, or it applied but its loan application was denied, or it applied and got less than 75%of the requested amount, or it refused the loan because the cost was too high. On average, thisde…nition applies to 14 percent of the …rms in our sample.
Figure 1 compares the evolution of actual credit constraints and of expectations of future credit
availability over time, for the …rms in our sample. It plots the change over the previous period in
the share of …rms that are not credit constrained, and the change over the previous period in theshare of …rms that expect credit availability to improve in the future. The Figure illustrates twovery important fact. First, while at the time of the OMT announcement, an almost equal share of…rms (around 20 percent) were experiencing a tightening in credit and a decline in their optimism
11

of future credit availability, right after the announcement both actual credit experience and credit
expectations started improving. Second, the improvement in expectations leads the improvementin actual credit experience. In particular, 1 year after the announcement, more …rms expect credit
conditions in the future to improve than to remain unchanged or deteriorate. However, it takes
nearly a year for a larger share of …rms to experience an improvement, rather than a stagnation ora deterioration, in actual credit access.
Figure 2 illustrates the same pattern for the two types of countries in our dataset, those notexperiencing …scal stress at the time of the OMT announcement, as well as those experiencing
one. The data clearly suggest that the pattern documented in Figure 1 is by and large driven by…rms in …scally stressed countries. This early evidence thus supports the idea that unconventionalmonetary policy targeting the balance sheets of banks can a¤ect both the actual credit experienceand the expectations of future credit availability of …rms borrowing from such banks. Importantly,
expectations lead actual experience. To the extent that expectations can have an independent e¤ect
on …rm activity, Figures 1 and 2 suggest that unconventional monetary policy can transmit to thereal economy faster than conventional analysis would suggest.
Recent studies aiming at identifying the transmission of monetary policy through bank lending
have typically relied on credit registers (e.g., Jimenez et al., 2012) or on syndicated loan data (e.g.,
Acharya et al., 2018). Relative to the former, the SAFE does not contain information on theuniverse of …rms, but only on a small representative sample of …rms, and relative to the latter,it does not have— for most …rms— multiple …rm-speci…c and bank-speci…c observations over time.
Nevertheless, our dataset has a number of unique advantages when it comes to identifying theimpact of unconventional monetary policy on …rm expectations. First and foremost, it contains
answers to questions on …rms’ expectations about the availability of …nancing in the short run.
This makes it the …rst dataset of its kind with information on …rm expectations for alternative
sources of funding. Second, the SAFE contains a small panel component of …rms which allows us
to perform tests with …rm …xed e¤ects that aim at eliminating omitted variable bias related tounobservable …rm-speci…c heterogeneity. Third, it contains information on …rm-speci…c outcomesthat other types of datasets do not have access to, such as information about innovative activities.

12

4

Empirical methodology and identi…cation

We investigate the impact of unconventional monetary policy on small …rms’ expectations aboutbank funding by comparing the evolution of said expectations around the time of the OMT announcement for …rms borrowing from banks with high balance sheet exposures to impaired sovereign
debt relative to …rms borrowing from banks with low such exposures.
To calculate the extent to which a …rm’s creditor is exposed to the unconventional monetary
policy shock, we …rst take data from Bankscope on banks’total sovereign bond holdings in 2012.
We next distinguish between …rms in countries with sovereign debt problems during the 2010–2012 period (Greece, Ireland, Portugal, and Spain— "stressed countries") and …rms in countries
without sovereign debt problems during the 2010–2012 period (Austria, France, Germany, and theNetherlands— "non-stressed countries").9
We use three sources of identifying variation in our analysis: the time before and after the
ECB’s OMT announcement; the cross section of …rms borrowing from banks with di¤erent balance
sheet exposures to sovereign bonds relative to their assets; and the issuer of sovereign bonds. We
estimate the following di¤erence-in-di¤erence-in-di¤erences (DIDID) model:

Pr ob(Bank f inancing will improveiscbt ) = '( 1 P ostt
+ 2 P ostt

Stressedc

Sovereign bonds
Assetsb

Sovereign bonds
Assetsb+ 3 Stressedc

Sovereign bonds
Assetsb+ 3 Stressedc

Sovereign bonds
Assetsb+ 4 Xiscbt +5 sct +

(1)

6 b + "iscbt

Bank f inancing will improveiscbt is a dummy variable equal to 1 if the …rm said "Will improve"in response to any of these two questions: "Could you please indicate whether the availability of
9

The choice of countries is motivated by the fact that all countries in the treatment group experienced severe
problems in accessing government bond markets over the sample period. In 2010, 10-year bond yields reachedlevels usually associated with a high probability of sovereign default: 1210 basis points (Greece), 950 basis points(Ireland), 750 basis points (Portugal), and 550 basis points (Spain). European policy makers recognized the severityof the sovereign problems in these …ve countries. Greece received a bailout from the EC and the IMF in May 2010,
Ireland received one in November 2010, and Portugal agreed on a bailout in May 2011. As mentioned above, theEuropean Central Bank instituted the SMP whereby in May 2010 it started buying (in secondary markets) Greek,
Irish, and Portuguese government debt, and in August 2011 it intervened in Italian and Spanish debt markets, too.
For comparison, yields on 10-year government bonds for the six countries in the control averaged 340 basis points at
the end of 2010, similar to yields on 10-year US treasury bills.

13

bank overdrafts, credit lines or credit cards overdrafts will improve, deteriorate, or remain unchanged over the next 6 months?", and "Could you please indicate whether the availability of bankloans (new or renewal; excluding overdraft or credit lines) will improve, deteriorate, or remain unchanged over the next 6 months?" It is equal to zero if the …rm responded "Will remain unchanged"
or "Will deteriorate" to both questions. P ostt is a dummy variable that captures the ECB’s OMTannouncement and is equal to 0 between 1st October 2011 and 30th September 2012 (survey waves
6 and 7), and to 1 between 1st October 2012 and 30th September 2013 (survey waves 8 and 9).10
Stressedc is a dummy variable equal to 1 if the …rm is domiciled in a stressed countries (Greece,
Ireland, Portugal, and Spain), and to 0 otherwise.

Sovereign bonds
is the ratio of sovereign bondAssetsbholdings to total assets— at the end of 2012— of bank b with whom …rm i in sector s in countryc has a credit relationship during the entire sample period. Data on these exposures come from
Bankscope. While Bankscope does not distinguish between domestic and foreign bond holdings,
the vast majority of sovereign bonds held by banks in the Euro Area are issued by the domestic
sovereign.11
We also include a number of controls to account for shocks to …rm expectations which are notrelated to the OMT announcement. Xiscbt is a vector of time-varying …rm-level control variables.
These capture any unobservable shocks to …rm i in sector s in country c during time t, such as…rm-speci…c growth, investment opportunities, or demand for credit by capturing the independent
impact of …rm-level heterogeneity related to size, age, turnover, ownership structure, etc. Ample
evidence points to a negative relation between pro…tability and the demand for external funds(Almeida and Campello, 2010). Therefore, we expect larger and older …rms, whose projects havematured, to have a lower demand for external …nancing.

sct is an interaction of country, sector,

and time (i.e., survey wave) …xed e¤ects (sectors are de…ned at the 1-digit SIC level). These net
out variation in …rm expectations that is common to all …rms in sector s in country c during timet (e.g., demand for Spanish agricultural products). Its inclusion also alleviates concerns that theobserved variation in …rm expectations is driven by global shocks that are common to all …rms
10

We deliberately choose a symmetric sample period around the OMT announcement that is long enough to allow
us to measure any material change in credit access. In robustness tests, we compare credit access six months before
and six months after the OMT announcement.
11
Using an ECB dataset on monthly holdings by 250+ Eurozone banks that distinguishes between domestic and foreign sovereign bond holdings, Ongena, Popov, and van Horen (2019) report that the share of domestic sovereign bondholdings out of total sovereign bond holdings for the median Eurozone bank at the time of the OMT announcement
was 0.97.

14

(e.g., a global repricing of risk).

b is a bank …xed e¤ect which is common to all …rms borrowing

from the same bank. It controls for all observable and unobservable characteristics of an individual
bank, such as capitalization, business model, risk appetite, etc. The combination of …rm-speci…cfactors and various …xed e¤ects addresses the concern that our estimates can be contaminatedby shocks to credit demand unrelated to the supply of credit. For example, while agency costproblems may have become less severe and/or growth opportunities may have improved more for…rms domiciled in stressed countries, this should be accounted for by the …rm-speci…c information
and by the country-sector-time …xed e¤ects. Finally, "iscbt is an i.i.d. error term. P ostt Stressedc ,P ostt , and Stressedc are not included in the speci…cation because their e¤ect on …rm expectations
is subsumed in the matrix of country-sector-time …xed e¤ects.

Sovereign bonds
is not included in
Assetsb

the speci…cation because its e¤ect on …rm expectations is subsumed in the bank …xed e¤ects.
The coe¢ cient of interest is

1 . In a classical DIDID sense, it captures the change in expectations

from the pre-treatment to the post-treatment period, for …rms borrowing from banks with large
sovereign exposures relative to …rms borrowing from banks with low sovereign exposures, in stressedversus non-stressed countries. A positive coe¢ cient would imply that all else equal, after the
OMT announcement expectations about the availability of bank …nancing improved more for …rms
borrowing from banks with large sovereign bond exposures in stressed countries.
We estimate the parameters of Model (1) using Probit. We cluster the standard errors at
the county level (Petersen, 2009), to account for the spacial correlation in the standard errors.
In robustness tests, we also employ OLS instead of Probit, as a way of dealing with a potential
incidental parameters problem.

5

Empirical results

5.1

The OMT and …rm expectations: Main result

In Table 2, we present the point estimate for Model (1) whereby we compare the change in expectations of future credit availability from the pre-OMT period to the post-OMT period, for …rms
borrowing from banks with larger versus smaller balance sheet exposures to impaired sovereign debt.
In terms of the precise sample period, we compare the period 1st October 2011 –30th September
2012 (survey waves 6 and 7) to the period 1st October 2012 –30th September 2013 (survey waves 815and 9). We are therefore comparing the evolution of expectations over the short-to-medium-term,
the one year after the OMT announcement relative to the one year before the announcement. We
thus allow for the e¤ect to build beyond an immediate short-term reaction, but we stop the sample
period before it becomes contaminated by later developments in the business environment and inmonetary policy.
We start, in column (1), with a version of Model (1) that includes …rm-speci…c covariates,
but is stripped from bank and country industry time …xed e¤ects. The model thus controls forobservable factors that can a¤ect a …rm’s expectations of future credit availability in the absenceof a monetary policy shock. At the same time, the exclusion of the …xed e¤ects makes it possible
to estimate the direct e¤ect of background factors that are common to di¤erent groups of …rms
and would thus be netted out by the …xed e¤ects. Such factors include the monetary shock itself,
the sovereign exposure of the main bank that the …rm is borrowing from, and the circumstances ofoperating in a …scally stressed country.
We …rst note that the data strongly reject the hypothesis that the OMT had no e¤ect on…rms’ expectations of future credit availability. In particular, we …nd that in the year after the
OMT announcement, …rms were on average 20 percent more likely to expect that bank credit
would improve in the next six months.12 This suggests that the OMT announcement generated
an improvement in …rm expectations of future credit availability across the board. At the sametime, on average expectations are lower for …rms operating in stressed countries and borrowing
from banks with large exposures, indicating that these are indeed the …rms that are hardest hit
by deteriorating bank lending conditions during the sovereign debt crisis (i.e., before the OMTannouncement).
Crucially, the estimate of

1 is positive and signi…cant at the 1 percent statistical level.

Thissuggests that after the OMT announcement, and relative to …rms in non-stressed countries, …rms
borrowing from banks with large impaired sovereign exposures were considerably more likely to
expect credit conditions to improve than otherwise similar …rms borrowing from banks with smaller
such exposures. The e¤ect is economically meaningful, too. The point estimate on the interaction
term is 0.031. Given an interquartile range for the ratio of Sovereign bonds / Assets of 5.12, the12

In all tables, we report coe¢ cients from marginal probit. Hence, they can be interpreted as the marginal change
in probabilities for a …rm for which all other control variables are at their sample means.

16point estimate implies that in the year after the OMT announcement, a …rm in a stressed country
was 15.8 percentage point more likely to expect bank credit conditions to improve— relative to a…rm in a non-stressed country— if before the announcement it was associated with a bank at the75th percentile of sovereign debt exposure, compared to a …rm associated with a bank at the 25th
percentile of sovereign debt exposure.
In addition, we …nd that a number of …rm-speci…c factors are correlated with …rm expectations,in the statistical sense. For example, the oldest …rms (more than ten years of age) are more likely tohave positive expectations of future credit availability, potentially because of their lower informational opacity (Berger and Udell, 1995; Cole 1998). Similarly, …rms with improving capitalization
and/or improving credit history (over the past 6 months) are more likely to be optimistic about
future credit conditions than …rms whose capital or credit history deteriorated or did not change.
This suggests that …rms know that banks use hard information in their credit granting decisions.13
In column (2), we estimate the fully saturated model with bank …xed e¤ects and country industry time
…xed e¤ects. The inclusion of these allows us to hold constant any background forces that a¤ect ina similar way all …rms borrowing from the same bank, and all …rms operating in the same industry
in the same country during the same time period. In this speci…cation, we …nd that the youngest…rms (age 2 or less) and …rms with improving capitalization and/or improving credit history are
more likely to be optimistic about future credit condition. Crucially, we continue …nding that theprobability that an otherwise similar …rm would have positive expectations of future credit availability increased signi…cantly more after the OMT announcement for …rms borrowing from bankswith a signi…cant exposure to domestic sovereign debt domiciled in stressed countries. The e¤ect
is once again signi…cant at the 1 percent statistical level, and economically larger than in column(1). The point estimate on the interaction term is 0.052, suggesting that in the year after the OMTannouncement, a …rm in a stressed country was 26.4 percentage points more likely to expect bankcredit conditions to improve— relative to a …rm in a non-stressed country— if before the announcement it was associated with a bank at the 75th percentile of sovereign debt exposure, compared toa …rm associated with a bank at the 25th percentile of sovereign debt exposure. The variables and…xed e¤ects included in this regression explain around 17 percent of the variation in credit access
13

While we do not …nd that …rm size matters, a …nding that runs contrary to the evidence in Hadlock and Pierce(2010), we are not looking at listed …rms, like they do, but at SMEs for whom age is potentially a more important
determinant of credit constraints than size.

17

over the sample period, which is an excellent …t for a regression without …rm …xed e¤ects.

5.2

The OMT and …rm expectations: Falsi…cation tests

The key identifying assumption of our DIDID approach is that in the absence of the OMT-driven
positive shock to bank funding costs for banks with relatively large balance sheet exposures to
impaired sovereign debt, all …rms would be subject to the same trend in expectations of futurecredit availability.14 This does not have to be the case, and the break in trends we report in Table
2 may have started already before the OMT announcement for reasons unrelated to sovereign
stress or to unconventional monetary policy. While we condition our tests on observables, ourempirical strategy would be compromised if the expectations of future credit availability for …rms
in stressed countries borrowing from banks with large sovereign balance sheet exposures started to
improve already before the OMT announcement. This could have happened for a variety of reasons
unobservable to the econometrician, such as better investment opportunities. If this were to be
the case, we might incorrectly interpret pre-determined trends as evidence of the positive e¤ect of
unconventional monetary policy.
To address this potential problem we take advantage of the fact that our original dataset islong enough to allow us to test our key identifying assumption explicitly. We now focus on survey
waves 6–7 which were conducted over the period 1st October 2011 –30th September 2012. As bothsurvey waves took place before the OMT announcement, we can apply our DIDID strategy to testfor di¤erences in credit access trends across …rms within the pre-OMT sample period. In practice,in column (1) of Table 3, we compare the change in expectations of future credit availability across…rms with credit relationships to more versus less sovereign-debt-exposed banks, in stressed versus
non-stressed countries. The estimates from this regression suggest that there was no di¤erence
in expectations across …rms exposed to di¤erent credit shocks coming from banks with di¤erent
degrees of exposure to impaired sovereign debt in the one year before the OMT announcement.
While positive, the point estimate on the interaction of interest is not signi…cantly di¤erent fromzero. This placebo test thus con…rms that the improvement in expectations we registered in Table
2 did not predate the announcement of the OMT program.15
14

See Roberts and Whited (2011) for details.
From the point of view of the theoretical mechanism we test in this paper— bank lending being a¤ected by theprice of a class of assets that bank hold on their portfolios— these results are not surprising. In the year before
15

18

Another potential challenge to our empirical approach is that the OMT announcement increased
general economic optimism in the corporate sector, and so the increase in optimism about futurecredit availability is a manifestation of a broad-based increase in economic con…dence. This would
still suggest that unconventional monetary policy has an impact on expectations of future credit
availability, but not through the bank lending channel, putting our empirical approach in question. To tackle this issue, in the next three columns we report estimates from Model (1) whereby
the dependent variables is the change in …rm expectations of future availability of other funding
sources: trade credit (column (2)), equity, (column (3)), and debt securities (column (4)). If ouridenti…cation strategy is correct, we should see no impact on these in a DIDID sense. If, however,the OMT a¤ected corporates through a simple con…dence channel, then relative to …rms in thecontrol group, the expectations of …rms in the treatment groups should improve with regard toother types of funding, too.
We …nd that there is no statistical di¤erence in changes in expectations about future availabilityof non-credit sources of …nance across …rms in stressed versus non-stressed countries, borrowing
from high-exposure versus low-exposure banks. This suggests that the e¤ect we document is indeed
con…ned to the bank lending channel of …rm funding expectations, and is driven by an expected
improvement in lending by banks that stand to bene…t the most from the OMT, in terms of funding
costs.

5.3
5.3.1

The OMT and …rm expectations: Robustness tests
Model robustness

In Table 4 we address a number of non-trivial concerns related to our choice of empirical model.
To begin with, the di¤erence-in-di¤erence-in-di¤erences approach we employ is predicated on aninteraction term, and calculating and interpreting marginal e¤ects in non-linear models with tripleinteraction terms is not straightforward (e.g., Ai and Norton, 2003). To address this issue, incolumn (1), we estimate Model (1) using OLS. The point estimate on the interaction term is 0.043,
and as in the probit version of the model, it is signi…cant at the 1 percent level. This implies that
the OMT, average sovereign bond yields hovered for a long time around higher levels, but were relatively ‡at: theaverage yield on a 10-year bond issued by the Italian, Irish, Spanish, or Portuguese government was 7.4 in the monthof the OMT announcement, and 7.6 a year earlier. For comparison, that same number was 4.9 a year after the OMTannouncement.

19

in the year after the OMT announcement, a …rm in a stressed country experienced a 22 percentage
point increase in the probability that it would expect credit availability to improve in the future,
relative to a …rm in a non-stressed country, if before the announcement it was associated with abank at the 75th instead of the 25th percentile of sovereign debt exposure.
Next, we note that although our DIDID speci…cations allows us to control for omitted variables
that a¤ect both the treatment and the control group in a similar manner, identi…cation of thecausal e¤ect requires controlling for any systematic shocks to the treatment group. That is, weneed to control for other shocks that might be correlated with the …nancial sector’s exposure tosovereign stress. For example, it might be the case that credit constraints mapping …rm-speci…cnet worth loosened di¤erently across the treatment and the control group around the time of the
OMT announcement. Our results so far can thus be the outcome of a mechanism whereby the
expectation about the allocation of loanable funds is largely driven by …rms’ knowledge of theirbalance sheet strength (Ashcraft and Campello, 2007).
We address this concern by controlling for such shocks explicitly. In column (2), we add interactions of all …rm-speci…c variables with the Stressed and the Post dummies. This procedures aims
at accounting for the possibility that the e¤ect of various empirical proxies for net worth, such as
age and size, is time-varying and our main explanatory variable may be picking up part of it. However, we …nd that association with a creditor with substantial balance sheet exposure to impaired
sovereign debt continues to explain a substantial portion of the variation in changes in credit accessafter the announcement of the ECB’s OMT program. At the same time, the magnitude of thecoe¢ cient declines by two thirds, suggesting that …rm-specifc shocks did have a contemporaneouse¤ect on …rm expectations of future credit availability around the time of the OMT announcement.
We next address the possibility that the shift in expectations that we observe is driven byshocks to …rms’demand for credit that are unrelated to shocks to credit supply. So far, we haveattempted to identify an expectations shock driven by changes in monetary policy by comparing…rms with credit relationships with banks that bene…ted a lot from the OMT-driven decline in
the yields of certain sovereign bonds to …rms with credit relationships with banks whose funding
costs were arguably not strongly a¤ected by the OMT. This identi…cation strategy also allows us tocontrol for a range of …rm-speci…c characteristics and for country sector survey wave …xed e¤ects,
ensuring that our results are not contaminated by …rm-speci…c factors such as size or age, or by20

general changes in country-sector-speci…c conditions, such as country-speci…c shocks to the demand
for real estate services. However, it can still be the case that during the sample period, agency costproblems are less severe and/or unobservable growth opportunities are better for …rms borrowing
from a¤ected banks.
We address this issue by isolating those …rms that are observed at least once before and at least
once after the announcement of the OMT Program. While the panel component in the SAFE is
too limited to allow us to include …rm …xed e¤ects in the primary regressions, there are 84 …rms
with full balance sheet information which also disclosed their main creditor, which are present atleast once in each sub-period, and for which the empirical proxy for expectations of future credit
availability changed between the pre-OMT and the post-OMT period. We can therefore run ourmodel on this limited sub-sample of panel …rms and include …rm …xed e¤ects, thereby addressing
lingering concerns about omitted variable bias related to time-invariant …rm characteristics thatcan be correlated with the demand for credit.
The point estimate from this modi…ed version of Model (1) is reported in column (3). We
continue …nding a signi…cant expectations e¤ect of the OMT on …rms with credit relationship withbanks exposed to impaired sovereign debt. The e¤ect is signi…cant at the 1 percent statisticallevel, and if anything numerically larger than in the baseline speci…cation. Importantly, this more
restricted test con…rms that variations in changes in expectations of future credit access after the
OMT are strongly related to creditors’ funding costs even in a speci…cation which controls forunobservable …rm quality.
One remaining concern is related to selection. We are so far calculating changes in expectations
for those …rms that have a credit relationship. An alternative approach would be to look at the
population of …rms with positive demand for credit. This alternative approach would incorporate
information on …rms that declare no demand for bank credit because they have enough internal
resources. About 43 percent of the …rms in the dataset declare a need for bank credit, while therest declare that they do not need bank credit because they have enough retained earnings to…nance their investment and day-do-day operations. We now focus on the sub-sample of …rms witha strictly positive demand for credit, and address the selection issue of them being a non-random
sample of the population by employing a two-stage Heckman model. In practice, in the …rst stage
we regress on all right-hand-side variables that we have used so far the probability that a …rm will21

declare a positive need for credit. We include an instrument that is then excluded in the second
stage, namely, a dummy variable equal to one if the …rm’s own outlook has improved in the past
six months, and to zero if it has not. We argue that this variable should satisfy the relevancecondition because a better outlook should increase the demand for funding and hence for credit,
and it should satisfy the exclusion restriction as it is unlikely that the bank can observe the …rm’s
improved outlook so quickly. We calculate the inverse Mills’ratio from the …rst-stage and include itin the second stage, which is now only based on 1,529 …rm observations. The results from the second
stage are reported in column (4), and they strongly con…rm that …rms with credit relationships
with banks positively a¤ected by the OMT were more likely to experience an improvement in their
expectations of future funding in the wake of the OMT announcement.
5.3.2

Sample robustness

We now address several concerns related to our sample choice. To begin with, our choice of themain sample period is one year before and one year after the OMT announcement. We believethat this sample period allows for a reasonable lag in the evolution of any improvement in bankfunding costs due to the OMT announcement to the real sector. Nevertheless, it might be thata shorter time frame would provide for a cleaner identi…cation of the e¤ect on credit access byminimizing any contamination by developments that took place over the course of the year after
the OMT announcement. To address this issue, in column (1) of Table 5 we adapt our empiricalmodels to test for a short-term OMT e¤ect whereby we compare …rm expectations of future credit
availability during the six months before the OMT announcement (wave 7) to expectations of futurecredit availability during the six months after the announcement (wave 8). This empirical strategy
reduces the number of available observations to 617. Nevertheless, we keep …nding a strong e¤ectof the OMT announcement on the probability that …rms borrowing from banks with signi…cantexposures to impaired sovereign debt would experience a larger improvement in their expectationsof future funding. The e¤ect is signi…cant at the 5 percent statistical level, and two-and-a-halftimes larger than the one reported in column (2) of Table 2, suggesting that the immediate e¤ectof the announcement is indeed much stronger than the medium-run e¤ect.
We also perform this exercise for three waves (1.5 years) on each side of the OMT announcement,
to gauge the long-term e¤ect (column (2)). The coe¢ cient on the triple interaction of interest turns
22

out to be remarkably stable, compared to the one reported in column (2) of Table 2. However,its statistical signi…cance declines sharply, suggesting that indeed the OMT announcement had ane¤ect on …rm expectations mostly in the short-to-medium term, rather than in the longer term.
We next address the concern that all announcements related to the OMT were made between
July and September 2012 before most of wave 7 was conducted— our pre-OMT period. It may
therefore not be appropriate to classify …rms in wave 7 as pre-OMT observations. While ourapproach is still superior to classifying them as post-OMT, we need to show that these observations
are not driving our results. To address this concern, we drop wave 7 from the analysis. Column (3)shows that the point estimate on the main variable of interest is still positive, as well as signi…cantat the 1 percent level.
One …nal consideration is related to the fact that Greece is an outlier in the sample: it is the only
country to have e¤ectively been shut out of international bond markets and to have experienced aquasi-default when private investors were asked in February 2012 to accept a write o¤ equal to 53.5
percent of the face value of Greek governmental bonds. We therefore test if our results are robust
to the exclusion of Greek …rms from the sample. The evidence reported in column (4) con…rms
that this is the case, and the point estimate on the coe¢ cient of interest is practically identical tothe one reported in column (2) of Table 2.
5.3.3

Robust dependent variable

In our analysis so far, we have attempted to gauge the impact of unconventional monetary policy
shocks on …rm expectations of future credit availability. We have so far de…ned the latter as adummy equal to one if the …rm believes that either "the availability of bank overdraft, credit lines
or credit cards overdraft" or "the availability of bank loans (new or renewal; excluding overdraftor credit lines)" will improve over the next 6 months. The variable is equal to zero if the …rm
responded "Will remain unchanged" or "Will deteriorate" to both questions.
In practice, however, these are conceptually di¤erent situations. Lines of credit (and overdraft
facilities and credit cards) are forward commitments (typically one year in the SME sector) thatprovide a revolving facility where …rms have the option to borrow (in total) any amount up to
the facility’s limit. These are typically used to fund working capital (i.e., accounts receivable andinventory) whose combined amount varies daily as do the total draws under the facility. Unlike
23lines of credit (and other revolving facilities such as overdraft and credit cards) which tend to berelationship-driven in the SME sector, bank loans are likely to be longer term, transactions-driven,amortizing loans used to …nance …xed assets like equipment and real estate (Berger and Udell,
1995). Moreover, collateralization of lines of credit with the underlying assets (accounts receivableand inventory) is more challenging than amortizing loans with equipment or real estate (e.g., Berger
and Udell 1995; Udell 2004; Cerquiero et al., 2016; Calomiris et al., 2017 ) New loans can be shortterm, while credit lines signal a longer-term commitment, therefore, an increase in the availabilityof the latter would signal a relatively higher con…dence by …rms in the willingness of banks toprovide credit in the future. These distinctions may related to the di¤erences reported in Table 1
that show that over the sample period, …rms are less likely to expect that the availability of credit
lines will improve than they are to expect that the availability of new bank loans will improve (11percent versus 17.6 percent).
In Table 6, we repeat our main empirical test for each individual component of our main
dependent variable. In column (1) we de…ne …rm expectations of improved future credit availability
as a dummy equal to one if the …rm said "Will improve" in response to the question "Could you
please indicate whether the availability of bank loans (new or renewal; excluding overdraft or credit
lines) will improve, deteriorate, or remain unchanged over the next 6 months?", and to zero if it
said "Will remain unchanged" or "Will deteriorate." Analogously, in column (2) we de…ne …rm
expectations of improved future credit availability as a dummy equal to one if the …rm said "Will
improve" in response to the question "Could you please indicate whether the availability bankoverdraft, credit lines or credit cards overdraft will improve, deteriorate, or remain unchanged overthe next 6 months?", and to zero if it said "Will remain unchanged" or "Will deteriorate."
The evidence reported in the table strongly suggests that following the OMT announcement,…rm expectations of credit access increased in both dimensions, for …rms borrowing from banks withlarger, relative to …rms borrowing from banks with smaller, impaired sovereign exposures. However,
the e¤ect is substantially larger for new loans than for bank credit lines and overdraft facilities.
A …rm in a stressed country was 21.4 percentage point more likely to expect the availability of
new loans to improve— relative to a …rm in a non-stressed country— if before the announcement it
was associated with a bank at the 75th percentile of sovereign debt exposure, compared to a …rm
associated with a bank at the 25th percentile of sovereign debt exposure. In the case of credit lines24and overdraft facilities, the same di¤erence is only 8.1 percent. The evidence thus strongly suggeststhat the monetary-policy-driven improvement in …rm expectations of future credit availability likelyre‡ects less optimism about working capital …nancing facilities than transactions-based …xed asset…nancing.

6

Real e¤ects

6.1

The OMT, …rm expectations, and real decisions

So far we have established that the announcement of the OMT Program had a positive impact
on expectations about the availability of bank credit in the future, for …rms whose banks were
exposed to impaired sovereign debt and thus plausibly bene…ted from the OMT-driven reduction
in sovereign bond yields. Ultimately, however, what really matters is whether such improvement
in expectations of future credit availability had an impact on SME decisions and outcomes. It
is important to determine whether the announcement of this particular unconventional monetary
policy by the ECB led to an increase in …rms’ activities. Furthermore, it is important to study
whether such "real e¤ects" from the improvement in funding expectations were focused on current
operations, or whether they also extended to …rms’future prospects (e.g., innovation).
Regarding the latter, while there is considerable evidence connecting venture capital …nancing
to innovation, it is important to note that there is also evidence that commercial banks play a role in…nancing innovation, particularly for smaller and external …nance-dependent …rms (e.g., Benfratello
et al., 2008; Ferrando and Lekpek, 2018). The SAFE questionnaire includes some information thatallows us to assess potential real e¤ects from the OMT a bit further. While it contains no questions
about investment, it does ask …rms whether they have recently engaged in a number of innovative
activities. The one most directly related to actual innovation is the question: "Has your …rm in
the past six months the …rm introduced a new or signi…cantly improved product or service to themarket?" We create a dummy variable equal to one if the …rm indicated that it did. To ensure
timing consistency, we focus on wave 9 which was conducted fully 1 year after the OMT. Table
1 reports that a non-negligible fraction of all …rms (35.5 percent) have engaged in some type ofinnovative activity over the course of the past half-year.
In the …rst column of Table 7, we present evidence from a test whereby we study the correlation25

between the probability that a …rm engaged in innovation in the past six months and the so-far
main dependent variable, the dummy for improving expectations of future credit availability. The
evidence strongly suggests that over the sample period, …rms that are experiencing an improvementin their expectations of future credit availability are more likely to make product improvements.
Numerically, …rms that expect the availability of bank credit to improve in the future are 4 percentage point more likely to engage in product innovation, corresponding to a 12 percent increaseover the sample mean.
We also match the …rms in the SAFE to the Amadeus database, which allows us to extract
information on their investment activities. We measure …rm investment as the log di¤erence intangible investment from the pre-OMT to the post-OMT period, and then regress this variable
on the dummy for improving expectations of future credit availability. The estimates from thisregression points to a robust link between expectations of future funding and investment activities(column (2)). Numerically, investment by …rms that expect the availability of bank credit toimprove in the future is higher by 1.3 percentage points than investment by similar …rms that
expect the availability of bank credit to decrease or to remain the same.
Importantly, in all regressions we also control for actual credit experience. In practice, we
include on the right-hand side a dummy variable Credit constrained equal to 1 if the …rm declareda positive demand for bank …nancing in the past 6 months, but it was discouraged from applying
because it expected to be rejected, or it applied but its loan application was denied, or it appliedand got less than 75% of the requested amount, or it refused the loan because the cost was toohigh. Firms that are credit constrained report lower innovative activities and investment levels.
Crucially, the impact of expectations of future credit availability on innovation and investment
exists independently of the e¤ect of actual credit constraints. Our data thus strongly suggest that
there is a distinct funding-expectations channel whereby unconventional monetary policy can a¤ect
the real economy.

6.2

Aggregate e¤ects

These results raise the natural question, how large is the aggregate economic e¤ect of changes
in expectations on …rm real decisions, over and above the impact of improving actual access to…nance. Focusing on investment, we make use of the coe¢ cient on …rm expectations in column
26

(2) of Table 7. First of all, we note that in the year after the announcement, the share of …rms
that experienced an improvement in expectations of future credit availability increased by around
26 percentage points, and the share of …rms that experienced an increase in actual credit accessincreases by around 6 percentage points. This correspond to around 20 percent of all …rms thathave experienced a change in expectations without experiencing a change in actual credit access,or three-quarters of all …rms that experienced an improvement in expectations. The point estimate
suggests a 1.3 percentage point increase in investment for the average …rm that experienced an
improvement in expectations of future funding. This implies that in the year after the OMTannouncement, aggregate investment in the economy increased by around 0.26 percentage points
due to the impact of the OMT announcement on …rm expectations.

7

Conclusion

In this paper, we examine the impact of one particular unconventional monetary policy tool— the
ECB’s government bond purchasing program, the OMT— on the variation in small …rms’ expectations of future credit availability. In particular, we investigate 1) whether …rms experienced an
improvement in their expectations of future credit availability after the announcement of the program, and 2) whether said improvement was bigger for …rms whose bank bene…ted more from theprogram because it had substantial exposure to sovereign debt, relative to similar …rms borrowing
from an una¤ected creditor. We also study how this improvement in expectations a¤ected …rms’
real decision, such as investment and innovation decisions. We do so for a sample of 2,123 SMEs ineight euro area countries, using a restricted access dataset containing rich balance sheet information
for individual …rms, information on expectations, and the identity of their main bank.
We …nd that the announcement of the OMT Program resulted in a strong short-term (sixmonths) and medium-term (one-year) improvement in expectations of future credit availability.
by …rms borrowing from banks with substantial balance sheet exposures to impaired sovereign
debt. Numerically, in the year after the OMT announcement, a …rm in a stressed country was
26.4 percentage points more likely to expect bank credit conditions to improve— relative to a …rmin a non-stressed country— if before the announcement it was associated with a bank at the 75thof sovereign debt exposure, compared to a …rm associated with a bank at the 25th percentile of27

sovereign debt exposure. Second, this e¤ect was particularly strong for …rms borrowing from bankswith signi…cant balance sheet exposures to impaired sovereign debt. This suggests the existence of afunding-expectations channel of unconventional monetary policy that works through bank balance
sheet strength. Third, …rms with higher expectations of future credit availability have higher
investment and are more likely to engage in innovation. This e¤ect is observed while controllingfor current credit conditions, suggesting that the funding-expectations channel can impact …rms’
real decision over and above …rms’actual access to …nance. Finally, we …nd that expectations of
future credit availability lead actual credit availability by 6 months to one year, suggesting that
empirical tests which fail to control for the role of …rm expectations can overstate the impact ofcredit access on …rm growth. Our results imply that unorthodox monetary policy can stimulate
the real economy through a funding-expectations channel, by reducing the riskiness of a class ofassets that weighs heavily on some banks’balance sheets.

28

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33

Table 1. Summary statistics
Variable

Observations

Mean

St. dev.

Min

Max

Firm expectations about future financing
Bank financing will improve
Bank loans will improve
Bank credit lines will improve
Trade credit financing will improve
Equity financing will improve
Debt securities financing will improve

2,123
2,123
2,123
1,871
696
443

0.245
0.176
0.110
0.130
0.097
0.113

0.430
0.381
0.313
0.336
0.296
0.317

0
0
0
0
0
0

1
1
1
1
1
1

Firm characteristics
Main bank’s sovereign bonds / Assets
StressedStand-alone firm
Individual- or family-owned
Size_1
Size_2
Size_3
Size_4
Age_1
Age_2
Age_3
Age_4
Turnover_1
Turnover_2
Turnover_3
Turnover_4
Own outlook better
Capital better
Credit history better
Credit constrained

2,628
2,628
2,627
2,628
2,628
2,628
2,628
2,628
2,568
2,568
2,568
2,568
2,592
2,592
2,592
2,592
2,548
2,611
2,549
2,628

5.119
0.534
0.823
0.699
0.138
0.241
0.219
0.402
0.003
0.017
0.084
0.895
0.226
0.243
0.290
0.240
0.244
0.292
0.248
0.142

4.273
0.499
0.382
0.459
0.345
0.428
0.413
0.490
0.057
0.130
0.278
0.306
0.419
0.429
0.454
0.427
0.429
0.455
0.432
0.349

0.007
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

20.597
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

Real decisions
11,612
0.336
0.472
0
1
New product14,805
0.248
0.229
0
0.968
Fixed assets / Assets
4,701-0.007
0.171
-0.5
1.041
Investment
Note: This table presents weighted summary statistics for the variables used in the empirical tests. The weights
restore the proportions of the economic weight (in terms of number of employees) of each size class, economic
activity and country and are applied to the variables derived from the survey. ‘Bank financing will improve’ is adummy variable equal to 1 if the firm believes that the availability of bank loans or of credit lines will improve in
the next six months. ‘Bank loans will improve’ is a dummy variable equal to 1 if the firm believes that theavailability of bank loans will improve in the next six months. ‘Bank credit lines will improve’ is a dummy variableequal to 1 if the firm believes that the availability of bank credit lines will improve in the next six months. ‘Tradecredit financing will improve’ is a dummy variable equal to 1 if the firm believes that the availability of financing
through trade credit will improve in the next six months. ‘Equity financing will improve’ is a dummy variable equal
to 1 if the firm believes that the availability of financing through equity (including venture capital or businessangels) will improve in the next six months. ‘Debt securities financing will improve’ is a dummy variable equal to 1
if the firm believes that the availability of financing through issued debt securities will improve in the next six

34

months. ‘Main bank’s sovereign bonds / Assets’ is the ratio of sovereign bond holdings to total assets of the firm’s
main bank, in percentage points. ‘Stressed’ is a dummy variable equal to 1 if the firm is domiciled in Greece,
Ireland, Portugal, or Spain, and to 0 if the firm is domiciled in Austria, France, Germany, or the Netherlands. ‘Standalone firm’ is a dummy variable equal to 1 if the firm is an autonomous profit-oriented enterprise. ‘Individual- or
family-owned’ is a dummy variable equal to 1 if the firm’s owner is an individual or a family. ‘Size_1’ is a dummy
variable equal to 1 if the firm has between 1 and 9 employees. ‘Size_2’ is a dummy variable equal to 1 if the firmhas between 10 and 49 employees. ‘Size_3’ is a dummy variable equal to 1 if the firm has between 50 and 249
employees. ‘Size_4’ is a dummy variable equal to 1 if the firm has 250+ employees. ‘Age_1’ is a dummy variableequal to 1 if the firm is less than 2 years old. ‘Age_2’ is a dummy variable equal to 1 if the firm is between 2 and 5
years old. ‘Age_3’ is a dummy variable equal to 1 if the firm is between 5 and 10 years old. ‘Age_4’ is a dummy
variable equal to 1 if the firm is 10+ years old. ‘Turnover_1’ is a dummy variable equal to 1 if the firm’s annual
turnover is less than €2 mln. ‘Turnover_2’ is a dummy variable equal to 1 if the firm’s annual turnover is between€2 mln. and €5 mln. ‘Turnover_3’ is a dummy variable equal to 1 if the firm’s annual turnover is between €5 mln.
and €10 mln. ‘Turnover_4’ is a dummy variable equal to 1 if the firm’s annual turnover is €10+ mln. ‘Own outlook
better’ is a dummy variable equal to 1 if the firm’s own outlook improved in the past 6 months. ‘Capital better’ is adummy variable equal to 1 if the firm’s capital improved in the past 6 months. ‘Credit history better’ is a dummy
variable equal to 1 if the firm’s credit history improved in the past 6 months. ‘Credit constrained’ is a dummy
variable equal to 1 if the firm declared a positive demand for bank financing in the past 6 months, but it was
discouraged from applying because it expected to be rejected, or it applied but its loan application was denied, or
it applied and got less than 75% of the requested amount, or it refused the loan because the cost was too high.
‘New product’ is a dummy variable equal to one if in the past six months the firm introduced a new or significantly
improved product or service to the market. ‘Fixed assets / Assets’ denotes the ratio of fixed assets to total assets.
‘Investment’ denotes the change in tangible assets over the past year divided by last year’s tangible assets.

35

Table 2. Unconventional monetary policy and firm expectations about future bank financing: Main test
Bank financing will improve(1)(2)
Main bank’s sovereign bonds / Assets × Post × Stressed

0.031***
0.052***(0.007)(0.014)Main bank’s sovereign bonds / Assets × Post
-0.041***-0.054***(0.005)(0.007)Main bank’s sovereign bonds / Assets × Stressed-0.035***0.016**(0.007)(0.009)Stressed × Post
-0.103***(0.031)Main bank’s sovereign bonds
0.045***(0.007)
Stressed
0.203***(0.028)Post
0.208***(0.020)Stand-alone firm
0.063
0.076
(0.055)(0.078)
Individual- or family-owned-0.013-0.016(0.051)(0.053)
Size_1-0.020-0.050(0.037)(0.062)
Size_2-0.012-0.012(0.017)(0.042)
Size_40.023
0.018(0.033)(0.029)Age_1
0.147
0.578***(0.146)(0.031)
Age_2-0.095-0.079(0.089)(0.110)Age_4
0.131*
0.103(0.075)(0.100)Turnover_1-0.0090.019(0.034)(0.051)
Turnover_2-0.044-0.014(0.039)(0.029)Turnover_4
0.005
0.006
(0.048)(0.045)
Capital better
0.080**
0.100*(0.030)(0.062)Credit history better
0.155***
0.140**(0.031)(0.052)
Country × Industry × Time FEs
No
Yes
Bank FENo
YesNo. Observations
1,630
1,529
R-squared
0.13
0.17
Note: This table presents difference-in-difference-in-differences estimates of firms’ beliefs that the availability ofbank financing will improve in the next six months. The model is estimated using probit. The estimation period isstth
1 October 2011 – 30 September 2013. See Table 1 for variable definitions and sources. All regressions use
sampling weights that adjust the sample to be representative of the population. All regressions include fixed36effects as specified. Standard errors clustered at the country level appear in parentheses. *** indicates significanceat the 1% level, ** at the 5% level, and * at the 10% level.

37

Table 3. Unconventional monetary policy and firm expectations about future bank financing: Falsification tests
Bankfinancing will
EquityDebt securities
improve: Pre- Trade credit
trend, two financing will financing will financing will
waves
improve
improve
improve
(1)
(2)
(3)
(4)
Main bank’s sovereign bonds / Assets × Post × Stressed

0.025
0.180
0.012
0.111
(0.024)(0.177)(0.025)(0.096)
Double interactions included
Yes
Yes
Yes
Yes
Firm-specific controls
Yes
Yes
Yes
Yes
Country × Industry × Time FEs
Yes
Yes
Yes
Yes
Bank FE
Yes
Yes
Yes
Yes
No. Observations
381
1,343
295
127
R-squared
0.02
0.23
0.21
0.42
Note: This table presents difference-in-difference-in-differences estimates of firms’ beliefs that the availability ofdifferent types of financing will improve in the next six months. The model is estimated using probit. The
st
th
st
th
estimation period is 1 October 2011 – 30 September 2012 (column (1)) and 1 October 2011 – 30 September
2013 (columns (2)–((4)). See Table 1 for variable definitions and sources. All other firm-specific control variablesfrom Table 2 are included in the regressions. All regressions use sampling weights that adjust the sample to be
representative of the population. All regressions include fixed effects as specified. Standard errors clustered at thecountry level appear in parentheses. *** indicates significance at the 1% level, ** at the 5% level, and * at the 10%
level.

38

Table 4. Unconventional monetary policy and firm expectations about future bank financing: Robust model
Bank financing will improve
Alternative
Panel
Heckman
OLS
shocksfirms
correction
(1)
(2)
(3)
(4)
Main bank’s sovereign bonds / Assets × Post × Stressed

0.043***
0.016**
0.381***
0.161***(0.014)(0.007)(0.142)(0.050)
Double interactions included
Yes
Yes
Yes
Yes
Firm-specific controls
Yes
Yes
Yes
Yes
Country × Industry × Time FEs
Yes
Yes
Yes
Yes
Bank FE
Yes
Yes
Yes
Yes
No. Observations
1,529
1,529
168
1,529
R-squared
0.19
0.19
0.60
0.17
Note: This table presents difference-in-difference-in-differences estimates of firms’ beliefs that the availability ofbank financing will improve in the next six months. The model is estimated using OLS (column (1)) and probit
stth(columns (2)–(4)). The estimation period is 1 October 2011 – 30 September 2013. See Table 1 for variable
definitions and sources. All other firm-specific control variables from Table 2 are included in the regressions. In
column (2), the regression includes an interaction of all firm-level variables with the Post dummy. In column (3),only firms observed at least once before the OMT announcement and at least once after the OMT announcement
are used. In column (4), a two-stage Heckman correction procedure is applied which incorporates informationfrom firms that declare no demand for a bank loan. All regressions use sampling weights that adjust the sample tobe representative of the population. All regressions include fixed effects as specified. Standard errors clustered at
the country level appear in parentheses. *** indicates significance at the 1% level, ** at the 5% level, and * at the
10% level.

39

Table 5. Unconventional monetary policy and firm expectations about future bank financing: Robust sample
Bank financing will improve
ExcludingExcluding
Short-runLong-runwave 7
Greek firms
(1)
(2)
(3)
(4)
Main bank’s sovereign bonds / Assets × Post × Stressed

0.123**
0.056*
0.074***
0.053***(0.067)(0.034)(0.025)(0.014)
Double interactions included
Yes
Yes
Yes
Yes
Firm-specific controls
Yes
Yes
Yes
Yes
Country × Industry × Time FEs
Yes
Yes
Yes
Yes
Bank FE
Yes
Yes
Yes
Yes
No. Observations
617
2,427
1,243
1,404
R-squared
0.26
0.19
0.17
0.17
Note: This table presents difference-in-difference-in-differences estimates of firms’ beliefs that the availability ofbank financing will improve in the next six months. The model is estimated using probit. The estimation period is
st
st
st
st
st
1 April 2012 – 31 March 2013 (column (1)), 1 April 2011 – 31 March 2014 (column (2)), and 1 October 2011 –th
stth
30 September 2013 (columns (3)–(4)). In column (3), observations from 31 March 2012 – 30 September 2012
are excluded. In column (4), all firms domiciled in Greece are excluded from the analysis. See Table 1 for variable
definitions and sources. All other firm-specific control variables from Table 2 are included in the regressions. All
regressions use sampling weights that adjust the sample to be representative of the population. All regressions
include fixed effects as specified. Standard errors clustered at the country level appear in parentheses. ***indicates significance at the 1% level, ** at the 5% level, and * at the 10% level.

40

Table 6. Unconventional monetary policy and firm expectations about future bank financing: Robust dependent
variable
Availability of bank loans
Availability of bank credit
will improvelines will improve(1)(2)
Main bank’s sovereign bonds / Assets × Post × Stressed

0.042***
0.016*(0.014)(0.010)
Double interactions included
Yes
Yes
Firm-specific controls
Yes
Yes
Country × Industry × Time FEs
Yes
Yes
Bank FE
Yes
YesNo. Observations
1,682
1,359
R-squared
0.19
0.23
Note: This table presents difference-in-difference-in-differences estimates of firms’ beliefs that the availability ofbank loans (column (1)) and the availability of bank credit lines (column (2)) will improve in the next six months.
stth
The model is estimated using probit. The estimation period is 1 October 2011 – 30 September 2013. See Table 1
for variable definitions and sources. All other firm-specific control variables from Table 2 are included in the
regressions. All regressions use sampling weights that adjust the sample to be representative of the population. All
regressions include fixed effects as specified. Standard errors clustered at the country level appear in parentheses.
*** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level.

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Table 7. Unconventional monetary policy and firm expectations about future bank financing: Real effects
New product
Investment(1)(3)
Bank financing will improve
0.039***
0.013**(0.013)(0.005)Credit constrained
0.061***-0.013*(0.020)(0.007)
Firm-specific controls
Yes
Yes
Country × Industry × Time FEs
Yes
Yes
Bank FE
Yes
YesNo. Observations
9,741
5,557
R-squared
0.05
0.09
Note: This table presents difference-in-difference-in-differences estimates of firms’ probability introduction of anew product in the past six months (column (1)) and of firms’ percentage change in tangible assets in the past year
(column (2)). The models are estimated using probit (column (1))) and OLS (column (2)). The estimation period isstth
1 October 2011 – 30 September 2013. See Table 1 for variable definitions and sources. All other firm-specificcontrol variables from Table 2 are included in the regressions. ‘Credit constrained’ is a dummy variable equal to 1 if
the firm declared a positive demand for bank financing in the past 6 months, but it was discouraged from applying
because it expected to be rejected, or it applied but its loan application was denied, or it applied and got less than
75% of the requested amount, or it refused the loan because the cost was too high. All regressions use samplingweights that adjust the sample to be representative of the population. All regressions include fixed effects as
specified. Standard errors clustered at the country level appear in parentheses. *** indicates significance at the 1%
level, ** at the 5% level, and * at the 10% level.

42

Figure 1. Credit availability and expectations of future funding: Full sample

Note: The Figure plots percentage changes, from one survey wave to the next, in actual access to credit and in
expectations of future funding, for all the firms in the sample. Data are weighted averages. The vertical line
indicates the timing of the announcement of the OMT Program. Source: SAFE.

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Figure 2. Credit availability and expectations of future funding: Stressed versus non-stressed countries

Note: The Figure plots percentage changes, from one survey wave to the next, in actual access to credit and in
expectations of future funding, for firms in stressed versus non-stressed countries. Data are weighted averages.
The vertical line indicates the timing of the announcement of the OMT Program. Source: SAFE.

44