Isoforma 1 del Transportador de Monocarboxilatos (MCT1) 18

In this section, we present the identification strategy employed to investigate how the sudden and sharp shock to sovereign securities triggered by the Greek bailout was transmitted to the credit supply through the bank balance sheet channel. To gain intuition on the main challenges of this analysis, we present a simple model of lending under costly external

financing in the spirit of Khwaja & Mian (2008).17

When external financing is costly due, for example, to asymmetric information in the whole- sale market (Stein 1998), a shock to the market value of sovereigns held in bank’s portfolio increases bank’s cost of funding and translates into a tightening of credit supply (bank lend- ing channel). The sovereign shock, however, may also lead to a simultaneous reduction of firm’s investment opportunities, lowering firms’ credit demand (e.g. Bocola 2013). Formally,

consider a simplified environment where a bank b lends to firm j using short-term funding

backed by sovereign securities or issuing bonds. Following a reduction of liquidation value

of sovereign assets from γ toτ (0≤τ ≤γ), the change in bank credit supplied by bank b to

firm j can be decomposed as follows:

∆Lbj =

1

αB+αL

(∆θj+ ∆θ+αBτ Gb) = β0+β1Gb +ρj (3.1)

where the parameter αL > 0 measures the concavity of the demand for loans with respect

to loan size, αB >0 captures the cost of external finance, ∆θ and ∆θj capture, respectively,

aggregate and idiosyncratic changes in the bank’s marginal return on the loan. Thus, we

can decompose the change in loans from t= 1 to t= 2 can be decomposed in three factors:

(i) an idiosyncratic firm-specific shock to the profitability or productivity of the borrower (ρj = _αB+1_αL∆θj); (ii) an economy-wide shock (β0 = _αB+1_αL∆θ); (iii) and the sensitivity of

credit supply to the balance sheet shock triggered by bank b holdings of sovereigns (β1Gb =

αBτ

αB+αLGb). The last term captures the bank lending channel, that is the reduction of credit

supply imputable to the transmission of the bank-specific shock driven by direct holdings of distressed sovereigns.

Equation (3.2) highlights a first, crucial difficulty in the estimation of the bank lending

channel. Since the firm-specific demand shift – ρj – is unobservable, an OLS estimator

capturing the effect of the sovereign exposure on lending will be biased if firm demand is correlated to bank-exposure to sovereigns. In particular, if banks with high sovereign

exposure systematically lend to firms with negative demand shock (corr(Gb,∆θj) <0), we

would have a downward bias in our estimates. This would falsely lead us to attribute the drop in credit to a credit supply shift, even when the overall effect is entirely driven by a

reduction of firms credit demand.18

Following Khwaja & Mian (2008) we circumvent the omitted variable problem by focus-

ing on firms with multiple lending relationships, and adding firm fixed effects (ρj) in a

first-differenced model to control for observable and unobservable idiosyncratic changes in demand-side factors which might be systematically correlated with lenders’ exposure to the

crisis.19 _{We bring Equation (3.1) to the data using bank b pre-bailout (2010:Q1) holdings}

of Italian sovereign securities to measure bank’s exposure to the shock (Sovereigns2010), and

18_{This systematic sorting between highly exposed banks and “weak” firms is not only a theoretical possibility}

but can be empirically relevant. Consider the case of poor areas within a country: in these areas banks may end up holding more sovereign asset on average because of lower investment opportunities. At the same time, they will lend to local firms, which may be weaker and therefore more sensitive to sovereign shocks.

19_{Studying a number of countries, Ongena & Smith (2000) and Detragiache et al. (2000) report that firms}

borrowing from one bank is the exception more than the rule. In the United States 55.5 percent of small and medium firms have more than one bank, and the median number of credit relationships established by them is two.

compare credit supply in the four quarters before April 2010, when tensions on the Italian

sovereign debt grew unexpectedly, to credit supply in the four quarter after it:

∆ln(loanbj) = β0I +β

I

1Sovereignsb,2010Q1 + ΓI ·Xb,2010Q1+ρj +bj (3.2)

where ρj is a firm fixed effect in a first-differenced data andXb,2010Q1 is a set controls mea-

sured at the pre-bailout period. To reduce concerns related to serial correlation of the errors

(Bertrand et al. (2004)) and to average out any seasonality effect (Duchin et al. 2010), we

collapse the quarterly data into a pre-shock and a post-shock average, and calculate the

before-to-after percentage change in loans granted from bankb to firmj as the log difference

of the two averages (∆ln(Loansbj)). The empirical model in Equation (3.2) is equivalent to

a Difference-in-Difference specification, where intermediaries with lower exposure to Italian debt are used as the control group for banks with higher exposure. A negative and statis-

tically significant value of the coefficient ˆβ1 indicates the presence of the lending channel

triggered by banks’ sovereign holdings.20

In an ideal experiment, the amount of pre-bailout sovereigns would be randomly assigned across banks. In reality, the holdings of sovereign securities in the portfolio of financial institutions and their credit supply are a function of a set of bank-specific characteristics

and asset management strategies (Gennaioliet al.2013). In fact, at the onset of the sovereign

crisis, bigger, less capitalized and less liquid banks held a smaller share of assets invested in sovereign securities, while financial institutions with more more deposits and less reliant on interbank funding held more (Table C.4 in Appendix C.9). The same characteristics are

correlated with changes in banks’ propensity to lend.21 To isolate the effect of sovereign

20_{Differently from previous studies (Khwaja & Mian 2008), our estimates of β}

1 should not be interpreted as

the elasticity of credit supply with respect to the shock to market value or liquidity of sovereign holdings. Instead,β1 measures the percentage change of a lender’s credit supply with respect to the other lenders of

the same firm, which results from his exposure to sovereign securities.

21_{Comparing the bank-level change in total corporate loans between our pre- and post-shock period, Table}

C.5 (Appendix C.9) shows that bigger and more profitable banks cut lending more than smaller and less profitable financial institutions. The same is true for banks with more stable sources of funding, those that

exposure on credit supply from other bank-characteristics which may be correlated with the overall sovereign holding and have a direct impact on lending behavior, we augment Model

(3.2) with the set of bank-specific controls (Xb,2010Q1), measured at the end of the last quarter

before the shock (2010:Q1). In particular, we include bank profitability, size, capitalization, funding (retail deposit and wholesale), liquidity, quality of lending portfolio, and status of the bank as a mutual bank.

The coefficient βI

1 1 estimated in equation (3.2) captures the pure intensive margin of the

bank lending channel, as the left-hand side variable measures the change in the stock of

loans granted by lender b to borrower j focusing only on relationships existing in both the

pre- and the post-shock period. Using a similar specification we can estimate the lending

channel along theextensive marginby testing whether banks more exposed to the shock were

also more likely to terminate ongoing lending relationships after the onset of the sovereign crisis:

Cut Creditbj =β0E +β

E

1Sovereignsb,2010Q1+ Γ·Xb,2010Q1+ρj+bj (3.3)

where Cut Creditbj is binary variable taking value one whenever the credit relation between

bank b and firm j in place at the end of 2010:Q1 was terminated within the following year,

after the onset of the sovereign crisis. The coefficient βE

1 tests whether, after the sovereign

shock, a firm is more likely to interrupt a lending relationship with those lenders more exposed to government securities after the sovereign shock.

The validity of this identification strategy relies on the following conditions. First, financial institutions should not have anticipated the imminent transmission of the sovereign crisis to the Italian debt and adjusted their sovereign portfolio beforehand. If the shock to Italian sovereigns were expected before the downgrade of Greece, holdings at 2010:Q1 might reflect

have higher liquidity holdings, and those that participate in the wholesale market as net borrowers. On the contrary, more capitalized financial institutions increased their corporate lending relative to less capitalized banks before-to-after the Greek bailout.

strategic adjustments undertaken in expectation of the imminent crisis, and confound the interpretation of our results. The stylized facts presented in Section 3.2 suggest that this was not the case. Before the downgrade of Greek debt both financial markets and media were not expecting such a sharp contagion of the sovereign crisis to Italy and other European countries. Moreover, the origin of the tensions on Italian sovereigns can be traced back to large government deficits and high public debt rather than imputed to a structural weakness

of its banking system (Acharya et al. 2011a; Lane 2012; Angelini et al. 2014).

Second, our quasi-experimental design also requires the parallel trend assumption to hold. In other words, we need to assume that, in the absence of the sovereign crisis, financial institu- tions with higher sovereign holdings (the treated group) would have displayed a credit supply trend comparable to banks with lower holdings (the control group). While the parallel trend assumption is fundamentally untestable due to the absence of an observable counterfactual, the next section presents and discusses extensive evidence that supports it.