Credit Supply and Productivity Growth

BIS Working Papers

No 711

 

Credit Supply and Productivity Growth

by Francesco Manaresi and Nicola Pierri

 

Monetary and Economic Department

March 2018

 

JEL classification: D22, D24, G21

Keywords: credit supply, productivity, export, management, IT adoption

 

This publication is available on the BIS website (www.bis.org).

 

© Bank for International Settlements 2017. All rights reserved. Brief excerpts may be reproduced or translated provided the source is stated.

ISSN 1020-0959 (print)

ISSN 1682-7678 (online)

 

Credit Supply and Productivity Growth

Francesco Manaresi

Nicola Pierri

 

Abstract

We study the impact of bank credit supply on firm output and productivity. By exploiting a matched firm-bank database which covers all the credit relationships of Italian corporations over more than a decade, we measure idiosyncratic supply-side shocks to firms’ credit availability. We use our data to estimate a production model augmented with financial frictions and show that an expansion in credit supply leads firms to increase both their inputs and their output (value added and revenues) for a given level of inputs. Our estimates imply that a credit crunch will be followed by a productivity slowdown, as experienced by most OECD countries after the Great Recession. Quantitatively, the credit contraction between 2007 and 2009 could account for about a quarter of the observed decline in Italy’s total factor productivity growth. The results are robust to an alternative measurement of credit supply shocks that uses the 2007-08 interbank market freeze as a natural experiment to control for assortative matching between borrowers and lenders. Finally, we investigate possible channels: access to credit fosters ITadoption, innovation, exporting, and the adoption of superior management practices.

JEL Classification: D22, D24, G21

Keywords: Credit Supply; Productivity; Export; Management; IT adoption


  • Bank of Italy - francesco.manaresi@bancaditalia.it
  • Stanford University - pierri@stanford.edu

We thank Nick Bloom, Tim Bresnahan, Liran Einav, and Matt Gentzkow for invaluable advice. We thank, for their insightful comments, Ryan Banerjee, Shai Bernstein, Barbara Biasi, Matteo Bugamelli, Rodrigo Carril, Francesca Carta, Emanuele Colonnelli, Han Hong, Pete Klenow, Ben Klopack, Leonardo Gambacorta, Simone Lenzu, Matteo Leombroni, Andrea Linarello, Francesca Lotti, Davide Malacrino, Petra Persson, Luigi Pistaferri, Paolo Sestito, Joshua Rauh, Luca Riva, Cian Ruane, Enrico Sette, Hyun S. Shin, Pietro Tebaldi, Christian Upper, and all participants at the Stanford IO workshops and applied economics seminars, the 2 nd Bay Area Conference, seminars at the Bank of Italy, the BB Seminars at the Italian Treasury Department, the CompNet Annual Conference, the BIS Research Meeting, and the BIS-IMF-OECD Conference. Francesco Manaresi developed part of this project while visiting the Bank for International Settlement under the Central Bank Research Fellowship program. Nicola Pierri gratefully acknowledges financial support from the Bank of Italy through the Bonaldo Stringher scholarship, from The Europe Center at Stanford University through the Graduate Student Grant Competition, and from the Gale and Steve Kohlhagen Fellowship in Economics through a grant to the Stanford Institute for Economic Policy Research. All errors remain our sole responsibility. The views expressed by the authors do not necessarily reflect those of the Bank of Italy.

 

1 Introduction

Does lenders’ credit supply affect borrowing firms’ productivity and, if so, how?

Aggregate productivity growth has declined in most OECD economies over the last decade, as illustrated by figure 1. While financial crises are found to induce strong and persistent recessions, it is still an open question whether credit supply (or lack thereof) played a major role in generating (and/or sustaining) this productivity slowdown.

In this paper, we estimate the effect of idiosyncratic changes in the supply of credit faced by Italian firms on their total factor productivity (TFP) growth. We focus on Italy because of the availability of detailed loan- and firm-level data on credit, inputs, and output. Khwaja & Mian (2008), Chodorow-Reich (2013), and Amiti & Weinstein (2017) exploit lender-borrower connections to provide evidence that negative shocks experienced by banks diminish credit supplied to borrowing firms and constrain those firms’ investment and employment. This paper extends the previous literature by looking at the impact of credit on productivity and by tracing its channels.

The sign of the causal relationship between the availability of external finance and productivity is theoretically and empirically ambiguous. Standard models of financial frictions assume that agents have an exogenous productivity, implying that credit constraints affect output only via reductions in the amount of capital used in production. Richer models can generate either a negative or a positive relationship. On the one hand, being forced to operate with fewer resources might spur innovation (Field, 2003)[5] and abundance might induce managers to stint their efforts or aggravate agency prob­lems (Jensen, 1986)

 

On the other hand, credit availability may have positive effects on firm productivity, as it might support productivity-enhancing strategies. Firms facing tighter credit constraints might invest less in R&D because of liquidity risk (Aghion et al. , 2010) and might acquire fewer intangible assets because it is more difficult to use them as collateral (Garcia-Macia, 2015). Credit-constrained firms might undertake less radical innovation (Caggese, 2016), while Midrigan & Xu (2014) emphasize the role of fixed costs. Additionally, negative credit shocks might hurt small firms by forcing man­agers/entrepreneurs to divert time and effort away from productivity improvements in order to create relationships with new lenders (“managerial inattention”).

We contribute to the relevant literature on four dimensions. First, we combine firm-bank matched data on credit granted by all financial intermediaries to all Italian incorporated firms over the period 1997-2013, with detailed balance-sheet information for a large sample of around 70,000 firms, to provide a complete picture of firm access to bank credit together with high-quality data on inputs acquisition and output for both large and small firms. Importantly, we are in a position to credibly study firm-level financial constraints without limiting our analysis to syndicated loans or public companies.

Second, we identify idiosyncratic credit supply shocks by exploiting two alternative empirical strategies: one based on bank-firm relationships and the other on a natural experiment. Unlike previous empirical studies on the link between finance and productivity, ours does not rely on self­reported measures of credit constraints, (potentially endogenous) proxies for financial strength, or local and industry-specific shocks (which might correlate with demand/technology dynamics). Our main empirical strategy decomposes the growth rate of credit of each bank-firm pair into firm- year and bank-year components. The bank-year component reveals how different banks change the quantity of credit granted to the same firm and captures shocks to bank supply. This additive decomposition, closely related to the ones developed by Amiti & Weinstein (2017) and Greenstone et al. (2014), rests on assumptions related to the matching between banks and firms and the structure of substitution/complementarity between lenders. We provide novel tests for these hypotheses.

To aggregate bank-specific credit supply shocks at the firm level, we exploit the stickiness of bank-firm relations. Because of relationship lending, one lender’s expansion or contraction of credit disproportionally affects its existing borrowers. As a result, two firms serving the same market might experience different shocks to their ability to finance their operations because of pre-existing credit relations with different lenders. We therefore average bank shocks at the firm level, using lagged credit shares as weights, to obtain a firm-specific credit supply shock. These shocks allow us to study the effect of credit on firm output and productivity both in “normal times” and during recessions.

Quantitatively, we find that a 1% increase in credit granted raises value-added TFP growth by around 0.1% and revenue TFP growth by 0.02-0.03%. During the financial crisis of 2007-09, credit growth shrank by around 12%: our estimates imply that a similar supply-driven credit crunch would have induced between 12.5% and 30% of the average drop in firm TFP experienced by Italian firms during that period. The effect of credit on TFP growth lasts up to two years and does not revert afterwards, so that the impact on TFP is persistent over time and can partly explain the sluggish productivity growth after the financial crisis. Large firms and firms with more lending relationships, which are probably more easily able to substitute away from contracting lenders, are largely unaffected by credit supply. Effects are stronger in sectors where bank credit is more important; that is, manufacturing and industries characterized by higher leverage. Our results imply that a credit crunch can generate a productivity slowdown by depressing firm-level TFP. This effect may persist for several years.

To rule out the possibility that results are driven either by assortative matching between firms and banks or other confounding factors or by some forms of reverse causality, we use a second empirical strategy, which exploits the freezing of the interbank market in 2007-08 as a natural experiment. This shock affected Italian banks differently (and unexpectedly) according to their pre-crisis reliance on this source of funding. We show that firms for which the credit crunch hit harder through their lenders experienced a lower growth rate of productivity afterwards. Firm exposure to the interbank market shock is found to be uncorrelated with pre-crisis growth potential and sensitivity to business cycle. This alternative identification strategy confirms the causal link between credit supply and productivity. Its estimated magnitude is significantly larger than the baseline estimates, suggesting that the productivity effects are stronger during financial turmoil.

Third, we argue that the standard production function estimation methods would not allow one to identify the causal effect of credit supply on productivity (see De Loecker (2013) for a conceptually analogous case regarding the effect of exporting on efficiency). Therefore, we enrich the production function estimation by allowing for heterogeneous credit constraints affecting both input acquisition and productivity dynamics.

Fourth, we augment our dataset with information from administrative and survey-based sources in order to show that several productivity-enhancing activities, such as R&D, patenting, export, inno­vation, adoption of information technology (IT), and introduction of superior management practices, are stimulated by credit availability. These strategies increase productivity both in the short-run (e.g., IT-adoption) and in the long-run (e.g., R&D). Therefore, their sensitivity to credit can explain the immediate effects of a credit supply shock on TFP and also suggests that there are additional effects over a longer horizon. Finally, we discuss some indirect evidence that is consistent with the “managerial inattention” hypothesis.

Our results imply that disrupting access to external funds depresses output above and beyond the observable contraction of investments. This contributes to the theoretical literature on the aggregate effects of financial frictions (Brunnermeier et al. , 2012) and to the empirical investigation of frictions and investment decisions (see Fazzari et al. (1988) and Rauh (2006)).

Our findings are also an important complement to the literature on the misallocation of production factors. This strand of research has been thriving in recent years, in particular, since the seminal paper by Hsieh & Klenow (2009).[7] It studies how frictions—financial ones in particular—affect overall productivity by shaping the allocation of capital and other inputs between firms for a given distribution of idiosyncratic productivity. We show such financial frictions alter the location of productivity distribution. Therefore, any empirical investigation of the effect of a change in financial conditions on productivity should take into account jointly the impact on the allocative efficiency of inputs and the direct effect on firms’ productive efficiency. Our results also imply that part of the vast heterogeneity in firms’ productivity, which has been consistently found in several empirical works (Syverson, 2011), may be traced back to unequal access to external funds.

We show that the relationship between credit supply and productivity is positive and concave. Negative shocks have larger effects than positive ones and credit supply is particularly important during a financial crisis. These empirical results highlight the fact that it is not only the quantity of credit that matters for productivity, but also its stability. Consequently, a credit crunch is likely to have a larger effect on TFP growth than a credit expansion of the same magnitude. Volatility of the banking sector’s credit supply is detrimental to firm productivity.

A large literature is interested in the link between finance and firm productivity. For instance, see Schiantarelli & Sembenelli (1997), Gatti & Love (2008), Butler & Cornaggia (2011), Ferrando &

Ruggieri (2015), Levine & Warusawitharana (2014), and recent papers by Duval et al. (2017), Dorr et al. (2017), Cavalcanti & Vaz (2017), and Mian et al. (2017). Other papers study the impact of credit on specific productivity-enhancing strategies, such as R&D (Bond et al. (2005), Aghion et al. (2012), and Peters et al. (2017a)), innovation (Benfratello et al. (2008) and Caggese (2016)), intangible investments (Garcia-Macia (2015) and de Ridder (2016)), and exporting (Paravisini et al. (2014) and Buono & Formai (2013)). Access to other sources of external funds can also affect produc­tive investments: for instance, Bernstein (2015) documents how IPOs change innovation strategies in the United States.

Section 3.1 describes the estimation of idiosyncratic credit supply shocks. Section 3.2 presents a partial-equilibrium model of firm production with heterogeneous credit constraints, which is used to back out firm-level productivity. Section 4 shows that credit supply affects firm input acquisition and output. Section 5 contains our main results and deals with their robustness, heterogeneity, and persistence. Section 6 presents additional evidence from the 2007-08 collapse of the interbank market. Section 7 investigates the mechanisms driving the effect of credit supply on productivity. Section 8 concludes.

2 Data

To perform our empirical analysis, we combine detailed balance-sheet data with loan-level data from the Italian Credit Register and survey-based information on productivity-enhancing activities.

2.1 Firm balance-sheets: The CADS dataset

The Company Accounts Data System (CADS) is a proprietary database administered by CERVED- Group Ltd. for credit risk evaluation. It has collected detailed balance-sheet and income statement information on non-financial corporations since 1982 and it is the largest sample of Italian firms for which data on actual investment flows are observed; net revenues of CADS firms account for about 70% of the total revenues of the private non-financial sector. Because this database is used by banks for credit decisions, the data are carefully controlled.

We estimate production functions for firms sampled in CADS from 1998 to 2013. Firm-level cap­ital series are computed applying the perpetual-inventory method (PIM) on book-value of capital, investments, divestments, and sector-level deflators and depreciation rates. Operating value added and intermediate expenditures are recorded in nominal values in profit-and-loss statements; we con­vert them in real terms using sector-level deflators from National Accounts. The baseline measure of labor is the wage bill, deflated using the consumer price index (CPI). Expenditures on intermediate inputs are deflated using a combination of sector-level deflator and regional-level CPI. Throughout the paper, we use a Nace Rev.2 two-digit definition of industry. In addition, in a robustness exercise (section 5.1), we show that our main results are very similar if we use a finer four-digit definition.

From CADS, we also collect information on firm characteristics such as age, cash-flow, liquidity, assets, and leverage (total debt over assets). Their lagged values are used throughout the analysis in section 5 as firm-level time-varying controls.

2.2 Firm-bank matched data: The Italian Credit Register

The Italian Credit Register (CR), owned by the Bank of Italy, collects individual data on borrowers with total exposures (both debt and collateral) above €30,00towards any intermediary operating in the country (including banks, other financial intermediaries providing credit, and special-purpose vehicles). The CR contains data on the outstanding bank debt of each borrower, categorized into loans backed by accounts receivable, term loans, and revolving credit lines. CR data can be matched to CADS using each firm’s unique tax identifier.

For all the credit relationships of any Italian incorporated firm and any intermediary between 1998 and 2013, we measure net credit flows as the yearly growth rate (delta-log) of total outstanding debt. We do not differentiate between different kinds of credit (for instance credit line versus loan), because the choice of which type of credit to increase/decrease is ultimately the result of strategic bargaining between banks and firms. We also focus on credit granted rather than on credit used, as the latter is more strongly affected by credit demand.

2.3 Additional data sources

While the baseline estimate of the effect of credit supply on productivity exploits CADS and CR, further enquiries into the channels that drive this effect and several robustness checks of our analyses rely on additional data sources.

To test whether estimates of credit supply shocks are robust to assortative matching between firms and banks (see section 3.1), we control for past interest rates charged by banks to firms. This information is available from the TAXIA database, administered by the Bank of Italy, for a large sample of Italian banks (encompassing over 70% of all credit granted to the Italian economy). Interest rates are computed as the ratio of interest expenditures to the quantity of credit used.

For our study of the consequences of the 2007-2008 interbank market collapse as an exogenous change in credit supply (section 6), we obtain information on banks assets, ROA, liquidity, capital ratio, and their interbank liabilities and assets from the Supervisory reports.

In Section 7, we study the relevance of specific productivity-enhancing activities that are fostered by credit supply. These include IT-adoption, R&D expenditures, patenting, and export. Such information is difficult to identify using balance-sheet data, because reporting by firms is generally non-compulsory. For this reason, we complement CADS with two sources of data. Data on IT- adoption, R&D, and export come from the INVIND Survey, administered by the Bank of Italy. INVIND is a panel of around 3,000 firms, representative of Italian firms with more than 20 employees and active in manufacturing and private services. For patent applications to the European Patent Office, we use the PatStat database. In particular, we exploit a release prepared by the Italian Association of Chambers of Commerce (UnionCamere), which matches all patent applications made during 2000-2013 with the tax identifiers of all Italian firms. We also obtain data on management practices for more than 100 manufacturing companies from the World Management Survey (see section 7).

2.4 Sample selection and descriptive statistics

Our main analysis is based on two samples. We use (a) a relationship-level dataset, in which an ob­servation corresponds to a bank-firm-year triplet, to identify credit supply shocks and (b) a firm-level dataset, in which observations correspond to firm-year pairs and credit supply shocks are aggregated across banks, to estimate production functions.

The relationship-level dataset is based on the CR data. It consists of all relationships between incorporated firms and financial intermediaries during 1997-2013. The resulting dataset consists of 13,895,537 observations and is composed of 852,196 unique firms and one 1,008 banks per year.

To estimate production functions, we consider all firms in CADS that report positive revenues, capital, labor cost, and intermediate expenditures, so that a revenue production function can be estimated. As a result, we exclude around one-fifth of the original CADS dataset: the final sample consists of 76,542 firms, corresponding to 656,960 firm-year observations. This dataset is used to estimate all the baseline regressions. Table 1 reports the main variables from the firm-level dataset for both the whole sample and for manufacturers.

To provide preliminary descriptive evidence that bank credit is a relevant source of finance for Italian firms, we study the credit intensity of firms’ activity. We define the credit intensity of firm i at time t as the ratio of total credit granted at the end of year t 1 to the net revenues of year t. On average, manufacturers are granted 43 cents for each euro of revenues generated, while this figure is only 34 cents for non-manufacturers. Appendix figure A.1 shows that credit-intense companies are larger in non-manufacturing sectors, but not in manufacturing. Appendix figure A.2 shows that industries with a higher capital-to-labor ratio are more credit-intensive.

3 Theoretical Framework

We investigate the relation between credit supply and productivity. As a first step, we consider an empirical model to disentangle idiosyncratic shocks to credit supply from shocks to credit demand and shocks to the general economic context (section 3.1). We then build a model of production with heterogeneous credit constraints to recover firm TFP (section 3.2).

3.1 Credit supply shocks​​​​​​​

We define a credit supply shock as any change in bank-specific factors affecting a bank’s ability and willingness to provide credit to firms. Banks are heterogeneous in their exposure to different macroeconomic risks (Begenau et al. , 2015). This heterogeneity can arise because of differences in liabilities, assets or capital.

Total credit granted to firm i at the end of year t equals the sum of credit granted by all existing intermediaries b : Ci,t = Y1 b Ci,b,t. We define firm i and bank b to have a pre-existing lending relation in period t if and only if Ci , b , t-i > 0. Credit granted Ci , b , t, is an equilibrium quantity which depends on both supply and demand factor, as well as on aggregate shocks. We collect all the observable and unobservable factors that determine the idiosyncratic supply of credit to corporations from bank b in year t into the vector Sb,t. For instance, bank-specific capital, cost of funds, and lending strategies may all be components of Sb,t. Similarly, let Di t be the vector of  observables and unobservables shaping firm i’s demand for credit and its desirability as a borrower, such as productivity, size, and leverage. In addition, credit may be affected by firm-bank specific factors, such as the length of the pre-existing lending relationship or the quantity of credit previously provided by the bank to the firm (affecting the incentive to evergreen). We collect these match-specific covariates in the vector Xibt. Finally, aggregate factors affecting all intermediaries and borrowers, such as aggregate demand or the monetary and fiscal stance, are collected in Jt.

Assumption 1

3 some smooth, unknown function C such that:

 

While this assumption is very general, it nonetheless limits the substitution patterns amongst different lenders. Indeed, it rules out the impact of other banks’ idiosyncratic shocks Sb/,t on credit granted by b to i. In appendix A.1, we show that the exclusion of other banks’ supply from equation (1) does not significantly affect our estimate of idiosyncratic credit supply shocks.

Log-linearizing equation (1) yields:

We define the credit supply shock of bank b in period t to be As'btc2. The idiosyncratic credit supply shock experienced by firm i in period t is a function of Asb tc2 for all the previously connected banks. Decomposition (2) can be written as:

where: jt is the mean growth rate of credit in the economy, 0bt is the change in credit granted explained by bank b’s supply factors, di,t is the change in credit granted explained by firm i factors, and ei,b,t is the sum of a matching specific shock Ax'ibtc3 and the approximation error approxi,b,t.

Assumption 2​​​​​​​

 

where D and S are sets of dummy variables indicating the identities of the borrower and lender.

Furthermore, without loss of generality, we normalize E [di,t] = E [0b t] = 0. We apply OLS to estimate equation (3.1). Under assumption 2, the bankxyear fixed effects (0b,t) are unbiased estimates of Asb,t. We focus on corporations having multiple relations in order to estimate bank- idiosyncratic shocks by exploiting within-firm-and-time variability. This allows us to condition for time-varying observables and unobservables at the borrower level.

Amiti & Weinstein (2017) (AW hereafter) study the identification of model (3.1). They show that assumption 2 holds without loss of generality, as long as one is willing to conveniently “relabel” the firm and bank fixed effects. That is, one can write the idiosyncratic component Axi,b,t as Axi,b,t = ai,t + bb,t + ei,b,t, where a and b are the linear projections of Axi,b,t on dummies for bank and firm identity and ei,b,t is uncorrelated with these dummies by construction. Therefore, bank fixed effects in (3.1) correspond to = 0b,t + c3 ■ bb,t, which are the parameters of interest in AW’s empirical analysis. In fact, AW show that the idiosyncratic match-specific terms do not affect the bank aggregate lending. In our study, however, we are interested in identifying the role of pure supply-side factors, Asb,t, so that the orthogonality assumption (assumption 2) does not come without loss of generality. In particular, it limits the interaction between demand and supply shocks (which enter the approximation error) and restricts the correlation between match-level covariates and bank or firm factors. We argue in appendix A.1 that this assumption is testable: we focus on two potential source of omitted variables in ei b t which may bias our estimate of supply-side shocks: substitution (or complementarity) patterns (such as those discussed in assumption 1) and relation characteristics. We show that our results on the impact of credit supply shocks on productivity are unaffected by the inclusion of these controls in the estimation of credit supply shocks. We therefore rely on the simpler specification in equation (3.1) for our main analysis.

In this paper, we study how borrowers’ inputs acquisition and output production are affected by lenders’ supply. Consequently, the cornerstone of the empirical strategy is a firm-level measure of credit supply shocks. To move from the bank-level measure of equation (3.1) to its firm-level coun­terpart, we rely on the intuition of the “lending channel” (Khwaja & Mian, 2008): borrower-lender relationships are valuable because they help mitigate information asymmetry, limited commitment, or other problems which might generate credit rationing (Petersen & Rajan, 1994). Consequently, they are sticky: changes in credit supplied by a bank have a disproportionally large effect on the firms with which it already has established credit relations. Obviously, a firm connected to a bank whose supply contracts can always apply to another bank for credit (see below). Yet, as long as credit from an unconnected bank is less likely or more costly, substitution between lenders will be imperfect. The empirical relevance of this phenomenon has been shown in several previous studies. We exploit this well-established fact to identify firm-specific credit supply shocks.

As a simple benchmark, we assume that the strength of a firm-bank relationship is proportional to the amount of credit granted. Therefore, we measure the shock to credit supply faced by firm i in period t as

 

A histogram of -i,t is provided in figure 2. Although the estimation of -b,t is performed considering only firms with multiple banking relations, the variable -i,t is defined for all firms which have some credit granted in year t — 1.

Two empirical findings validate this measure of credit supply shocks. First, we expect a positive supply shock to decrease the number of loan applications to new lenders, while we expect a positive demand shock to increase these applications. Appendix A.2 shows that an increase of our measures of credit supply shock is indeed associated with fewer loan applications on both the intensive and extensive margin. Second, appendix D shows that our measure responds negatively to the freeze of the interbank market, which was the trigger of the credit crunch in Italy (see section 6 for details). In appendix A.3, we study the relation between credit supply shocks and some determinants of bank credit supply, such as the crowding-out of sovereign debt, M&A episodes and balance-sheet strength, and we present qualitative results in line with economic intuition and previous literature.

3.2 Production with heterogeneous financial frictions​​​​​​​

We propose an empirical model to estimate firms’ production functions and recover their idiosyncratic productivity. We augment the classical production function estimation framework with a control function (Ackerberg et al. , 2007) by adding two elements: a set of credit constraints and a modified law of motion for productivity dynamics. This section presents the main elements of the model; details can be found in appendix B.1. Uppercase letters denote variables in levels, while lowercase letters denote natural logarithms.

Firm i operating in industry s, in year t, combines capital (ki,t), labor (1i,t), and intermediate inputs (mi,t)—which are also referred to as “materials”—to generate sales, (Yi,t) according to an industry-specific production function f (■), known up to a set of parameters As. Each firm has an idiosyncratic Hicks-neutral productivity wi,t:

 

As is common in the literature (Olley & Pakes, 1996), we assume that productivity can be decomposed into a structural component (tDi,t) and an i.i.d. error term (eft), which is unknown to the firm when production decisions are made:

Intermediate inputs are flexibly chosen every period in order to maximize variable profits (sales minus cost of labor and intermediate inputs). Then, if firm i is unconstrained, the amount of materials munc will solve:

where Pp[ is the price of materials faced by firm i, which might depend on its location p. In section 4, we provide evidence that firms acquire less inputs when they receive negative credit supply shocks. Relying on the first-order condition in (4) would be misleading if firms face heterogeneous credit constraints. Therefore, we allow for the possibility that intermediate inputs (and other inputs) face financially generated constraints:

 

where Bj,t-i is previous-period debt and r is an unknown function. Similar constraintare standard in the literature on financial frictions, such as Moll (2014), Buera & Moll (2015), and Gopinath et al. (2017), and they can be micro-founded by several market failures. We innovate by allowing them to depend on firm TFP and credit supply shocks. The results of the paper hold if we exclude credit rationing and, alternatively, if we assume that firms face heterogeneous costs of external funds. High-productivity firms might be considered more reliable borrowers and might therefore be allowed to borrow more, ceteris paribus. We thus assume that r is strictly increasing in its third argument. The quantity of intermediate inputs acquired by firm i is:

where m (•) is unknown and Xj,t is a vector containing firm-level inputs (capital, lagged capital, and labor), prices, and lagged debt. Under standard assumptions, the optimal value of materials is increasing in productivity cU,t, equation 5 can therefore be “inverted” (see Olley & Pakes (1996) and Levinsohn & Petrin (2003)). That is, there exists an (unknown) function h such that:

Therefore, log sales can be written as:

where T (xj,t, mj,t, 0j,t) _ h (xj,t, m^, 0j,t) + f (lj,t, fcj,t, miit, ^s). Following the previous literature, we assume a law of motion for productivity:

where It-1 is the firm information set at t —1 and (•) is unknown. We innovate by allowing credit supply to affect productivity dynamics. It would not be correct to estimate the production function without including financial frictions in the productivity dynamics and regress the productivity resid­uals on financial variables. An analogous problem is highlighted in De Loecker (2013) discussion of the measurement of productivity gains from exporting. Let us also define the productivity innovation as Zi,t :_ (Dj,t — E [cDi t|Xt-1]. Equation (6) implies moment conditions:

 

where Zj,t-1 contains lagged values of investments, labor, materials, and other variables. Estimation of the model is performed in two stages. In the first stage, we estimate the function T as Tj,t _ E [yi,t|xi,t, mj,t, 0j,t]. In the second stage, we rely on (7) to estimate the structural parameter of interest ^s. Table A.2 presents some descriptive statistics. Finally, we can back out firm-level productivity as residuals.

Therefore, this paper is about the ability of firms to transform inputs into sales and value added and not (only) about their technical efficiency. Our measure of productivity is referred to as “productivity” in several empirical studies, such as Olley & Pakes (1996), and as tfprrr (or “regression-residual total factor revenue productivity”) in Foster et al. (2017). Furthermore, our measure of productivity is proportional to the empirical estimate of (log) TFPQ (or “total factor quantity productivity”) in Hsieh & Klenow (2009). Our choice in this regard is somewhat constrained, as no firm-level data on product-level prices are available to economists for a sufficiently large number of Italian firms. Appendix C.1 provides a more detailed treatment of the topic and contains a brief discussion of the pros and cons of relying on revenues to estimate TFP.

4 Credit Supply Shocks and Firm Production

Is a firm’s production affected by the credit supply of its lenders? If credit frictions are not important the amount of credit a firm receives should be unaffected by the supply shocks of its lenders. In a frictionless world, a firm’s policy function might be affected by aggregate financial conditions but should not be shaped by the idiosyncratic shocks hitting any specific lender. Therefore, we estimate:

 

 

where Xj,t is either the log of total credit granted to firm i or a measure of output (log value added or net revenues) produced by firm i during year t or a measure of (log) input. The Y terms are firm and year x industryx province fixed effects. The former control for firm-specific unobserved heterogeneity which might affect both financial conditions and production. The latter capture local and sectoral demand and technology shocks, which might create spurious correlation between credit supply and firm dynamics. Results are presented in Table 2. Firms connected with banks expanding their supply of credit show higher growth of credit received, inputs acquired, and output produced than to other firms operating in the same market. The elasticity of credit granted with respect to the firm-level credit supply shock is approximately equal to 1. This allows for simple interpretation of the magnitude of all the main specification of this paper: a one-percentage-point increase in 0j,t is the change of credit supply necessary to increase the average credit granted one percent.

5 The Effect of Credit Supply on Firm Productivity Growth

Is firm productivity growth affected by the credit supply of its lenders? After identifying firm-level measures of credit supply shocks (section 3.1) and measuring TFP (section 3.2), we now tackle the main research question by estimating the model:

where: Aaj,t is the growth (delta log) of the Hicks-neutral productivity for firm i between years t — 1 and t and %,t is the weighted average of credit supply shocks of i’s previous-period lenders. The Y terms are firm and year x industryx province fixed effects. The former control for firm-specific unobserved heterogeneity which might affect both financial conditions and production. The latter capture local and sectoral demand and technology shocks, which might create spurious correlation between credit supply and firm dynamics. Results are shown in Table 3. One observation is one firm per year in CADS for 1998-2013, subject to the selection criteria detailed in section 2.4. In each column, we consider productivity growth as obtained from a different production function estimation. The two columns on the left use value added as a measure of output, while productivity in columns 3 and 4 is based on net deflated revenues. Columns 1 and 3 are based on the Cobb-Douglas functional form, while 2 and 4 are based on Trans-Log production functions. The top panel presents results for the whole economy, while the bottom panel focuses on manufacturers. All specifications clearly show that an increase in credit supply boosts productivity growth. A credit supply shock of one percentage point induces an increase in the growth rate or value-added productivity of approximately one-tenth of a percentage point for the whole economy and 0.13 points for manufacturing. The effect on the revenue based measures of productivity is between 0.02 and 0.03 percentage points. The difference between the size of the effect of credit supply on value-added productivity growth and the size of its effect on revenue productivity growth can be partially explained by the fact that, in our sample, the standard deviation of the former is more than three times that of the latter.

The magnitude of the effects is economically large. For instance, the drop in the total growth rate of credit granted between 2007 and 2009 is around 12% in our sample. Over the same period, (mean) value-added productivity growth declined by a bit more than 8% and revenue productivity growth declined by 1%. Therefore, if the drop in credit was fully driven by supply, it would explain between 12% and 30% of the productivity drop over the same period. These figures are likely to be conservative estimates; below we show that the productivity effects of credit shock are persistent
and that credit supply is particularly valuable during financial turmoil.

Appendix figure A.8 reports the bootstrapped distribution of the estimated effect of credit supply shock on productivity. The production functions are re-estimated for each bootstrap sample. All coefficients are above zero. This finding indicates that the sampling error in estimating productivity dynamics does not distort statistical inferences based on Table 3.

5.1 Robustness

This paper argues for a causal interpretation of the estimated relations between credit supply and firm productivity growth. We provide a broad set of robustness exercises to support this claim. Table 4 contains the relative results for the Cobb-Douglas revenue productivity case. Column (1) reports the baseline estimate (as in Table 3). Column (2) adds a set of lagged controls: a polynomial in assets size and the ratios of value added, cash flow, liquidity, and bank debt to assets. The inclusion of such controls has negligible impact on the estimated coefficients.

Estimates of equation (9) face both identification-related threats and measurement threats. This section deals with the latter and with potential problems related to the estimation of the productivity dynamics. Measurement error issues are discussed in appendix C.3. Analogously to the “peer effect” literature (Bramoulle et al. , 2009), three main threats may hamper our identification strategy of credit supply shocks based on firm-bank connections: reverse causality, correlated unobservables, and assortative matching. That is, %,t can be correlated with the error term in equation (9) because (a) connected agents are subject to correlated shocks, (b) lenders might decrease credit supply when expecting their borrowers to experience lower productivity growth, or (c) banks which are expanding their supply of credit are more likely to establish lending relations with firms that are increasing their productivity. The productivity shocks received by sizable borrowers might be the very reason why their lenders contract the supply of credit. That is, if banks have information about the future profitability of some particularly significant borrowers, they might preemptively decrease the supply of credit to all borrowers. We define an “important” borrower as any firm which, at any point between 1997 and 2013, accounts for more than 1% of the credit granted by any of its lenders. We then estimate model (9) excluding such firms. Results are reported in column (3) of Table 4, which shows that the estimated effect of credit supply shocks on productivity growth is unaffected by the exclusion of the borrowers that are most likely to lead to reverse causality, thus mitigating this concern.

A further concern is that connected borrowers and lenders might be affected by correlated un­observable shocks. In particular, the output market of the borrower might overlap with the lender’s collection or lending market. For instance, a drop in local house prices might contemporaneously lower consumption and also affect the value of collateral backing lenders’ loans. Since we measure revenue-based productivity, any demand shock might increase markups and be picked up as a change in productivity. To investigate the relevance of correlated unobservables for our results, we compare specifications with two different fixed-effects structures:

The first specification includes industryxprovincexyear fixed effects, which aim to control for demand and technology shocks. The second includes only includes only industry, province, and year fixed effects; it therefore allows only for nationwide economic fluctuations. Results are reported in columns (1) and (5) of Table 4. The magnitude of the coefficient is remarkably stable across the two specifications, despite the fact that the inclusion of the finer grid of fixed effects doubles the R2. This finding reveals that, if any unobservable is affecting both credit supply shocks and productivity, then it must be orthogonal with respect to location or industry. Since credit activity is indeed concentrated at the local (and/or industry) level, this is extremely unlikely to happen. Consequently, we can reasonably conclude that correlated unobservables are not driving our results. A formal econometric treatment of this intuitive argument is provided by Altonji et al. (2005) and Oster (2016). In appendix C.2, we provide bounding sets for the coefficient of interest, following Oster (2016), and show that they do not contain zero. Therefore, our results are “robust” to the presence of unobservable shocks. Furthermore, column (4) of Table 4 shows that firm fixed effects, while useful to control for firm-level unobservable characteristics, are not essential to our results.

The estimated effect of credit supply on productivity growth is similar to that in the baseline specification of column (1), providing no evidence that assortative matching explains our results. Finally, section 6 exploits a natural experiment to confirm that credit supply affects productivity; this, together with relative placebo tests, should eliminate residual concerns.

The bank-level credit supply shocks are computed using information on all borrowers. Therefore, if firm i has a lending relation with bank b, then its credit supply is estimated from a linear regression including observations relative to the amount of credit granted by b to i (see section 3.1). This could generate problems in small samples. Therefore, we estimate an alternative set of bank-level credit supply shocks using a “split sample” procedure. Column (7) presents estimates of the baseline specification using the “split sample” credit supply shock as an instrument. The similarity between estimates in columns (1) and (7) confirm that, since we rely on the universe of credit relations, this (potential) finite-sample bias is not a concern.

Estimation of production function parameters is a difficult exercise involving several (strong) as­sumptions, such as the absence of measurement error on inputs and a Markovian structure for the productivity dynamics. We perform several exercises to show that the specific modeling choices of section 3.2 do not affect the estimated effect of credit supply on productivity growth either qualita­tively or in terms of its magnitude. First, we re-estimate both the production function and equation using a finer four-digit industry classification (the baseline uses two-digit classification). Results are reported in column (8) of Table 4, which mitigates the concern that heterogeneity in the shape of the production function is a main driver of the baseline specification. Second, we re-estimate the production function by controlling for endogenous exit as in Olley & Pakes (1996). Column (9) of Table 4 shows that the magnitude of the relation between credit supply shocks and productivity is unchanged. Furthermore, we compare our results to traditional estimation techniques. Column of Table 4 reports results from the production function estimated with the cost-share procedure (Foster et al. , 20 1 7). Results are in the ballpark of the baseline estimation.

An alternative approach is to refrain from estimating the production function and, instead, study how the estimated effect of credit supply shocks on productivity varies as a function of the unknown parameters of the production function. The simplest production function is a Cobb-Douglas in value added:

where p disciplines the returns to scale and is the (relative) elasticity of value added to capital. Then, given a pair (p,), we can back out productivity as

and estimate y(P, ) as the coefficient of

We let p vary from 0.3 to 2 and from 0.01 to 0.9, so that our grid encompasses any plausible values of the return to scale and the elasticity of value added to capital. Results are presented in graphical form in figure 5, showing that we find a positive (and statistically significant) effect of credit supply shocks on value-added productivity growth for any point on the grid. Moreover, while higher values of the parameters tend to decrease the point estimates, y(p, ) stays between 0.07 and 0.1 within the whole support.

The collection of evidence reported in this section clarifies that any misspecification of the pro­duction function estimation, although it might bias the point-estimate of the effect of credit supply on productivity, is unlikely to change its magnitude significantly.

5.2 Heterogeneity​​​​​​​

Are all firms equally affected by credit supply shocks? A firm’s size might be a good predictor of its ability to find alternative sources of credit in case current lenders dry up. Furthermore, larger firms are less likely to be credit-constrained in the first place. Therefore, for each year, we compute an indicator for whether or not a firm is in the top quartile of the size distribution in terms of asset value or number of employees. Then, we estimate the equation:

 

Results are reported in columns (1) and (2) of Table 5, which refer to Cobb-Douglas revenue productivity. The parameter Ybig is estimated to be negative, indicating that large firms are less af­fected by credit supply shocks. The difference between the two groups is much larger and statistically significant in manufacturing.

Furthermore, we are interested in understanding whether having a larger number of lenders might help firms find sources of finance in case of negative credit supply shocks. Therefore, we estimate the model by allowing the coefficient to be different for firms in the bottom quartile for number of lending relations during the previous period. Results in column (3) document that borrowers with fewer lenders are much more affected by credit supply shocks.

An important dimension of the relevance of credit supply shocks is firms’ reliance on external funds. We classify industries as above and below the median according to the mean leverage (debt over assets) in the sample. Column (4) of Table 5 shows that the effect of credit supply shocks on revenue productivity is stronger in sectors with high leverage. Perhaps surprisingly, we do not find any significant pattern when analyzing heterogeneity according to sectoral cash flow over assets (see column (5)).

5.3 Persistence

The effect of credit supply on productivity is persistent. We define the innovation to the credit supply as Z_ft :_ 0j,t — E [0i,t|0t-i]. Then, we estimate the model

We choose T _ 3, since our empirical strategy is not fit to estimate the regression at a longer horizon Figure 4 graphically displays the coefficients, yt, for firms active in manufacturing (bottom panel) and all industries (top panel). They document that the peak in productivity is experienced one year after the shock and that the effect remains positive and significant for at least four years. This finding underlines that a temporary credit contraction can have persistent effects on productivity. It also rules out the potential concern that the effect we measure on revenue productivity is short-lived and due to factor hoarding caused by adjustment costs of labor and capital.

We do not find any statistically significant pre-trend. Our main results rely on pre-existing lending relations being orthogonal with respect to non-financial productivity shocks. Therefore, the absence of a pre-trend supports the claim that credit supply shocks have a causal effect on productivity.

5.4 Concavity of the credit-productivity relationship

The main goal of this paper is to measure and explain the productivity effects of changes in the quantity of credit supplied, focusing on its first moment: is more credit bad or good? This section, instead, investigates the shape of the relation between productivity and credit supply shocks, in order to understand whether higher moments of the distribution of credit supply shocks might have an impact on average firm productivity.

We divide 0j,t into quintiles q _ 1, 2, 3, 4, 5 and estimate:

where 1 (0j,t G q) is an indicator function taking value 1 iff the credit supply shock of firm i in year t belongs to the qth quintile of its distribution; the third (or median) quintile q _ 3 is the omitted category with y3 _ 0. Results are shown in graphical form in figure 6. The relation between credit supply and revenue productivity seems to be concave. That is, firms connected with banks with a relatively low supply of credit experience lower revenue productivity growth than their competitors; firms connected to banks with a particularly strong increase in credit do not grow at a particularly high rate. It is important not to be connected with banks experiencing bad credit supply shocks, but it is not useful to be connected with banks increasing their supply of credit particularly quickly. To strengthen this intuition, we re-estimate equation (11), which is used to study the persistence of credit supply shocks, by differentiating between positive and negative shocks. Figure 7 presents the results in graphical form. The coefficients relative to negative credit supply shocks are shown with negative values. The effect of credit supply shocks on productivity is driven by firms connected with banks experiencing relatively negative credit supply dynamics. Additionally, we argue in section 6 that credit supply shocks are particularly important when credit dries up.

6 The Interbank Market Collapse as a Natural Experiment

The credit supply shock derived in section 3.1 has the value of being general, in that it can be attributed to all firms (both multiple- and single-borrowers) and measured in any year for which there is bank-firm data on credit granted. This feature is exploited in section 7. Furthermore, the panel variation of 0j,t is essential for production function estimation (see section 3.2). However, since its construction relies on firm-bank connections, estimates of equation (9) might suffer from the identification problems highlighted in section 5.1. Although we have already discussed several robustness exercises to mitigate such concerns, here we propose an alternative strategy to strengthen the robustness of our results. We use the 2007-2008 market collapse of the interbank market as a specific “natural experiment” in which credit supply shifts were arguably exogenous with respect to firm observed and unobserved characteristics.[1] In addition, such variation came unexpectedly both to lenders and to borrowers, thus overcoming the problem of assortative matching.

The interbank market is a critical source of funding for banks: it allows them to readily fill liquidity needs of different maturities through secured and unsecured contracts. Total gross interbank funding accounted for over 13% of total assets of Italian banks at the end of 2006. Market transactions began shrinking in July 2007, when fears about the spread of toxic assets in banks’ balance sheets made the evaluation of counterparty risk extremely difficult (Brunnermeier, 2009); the situation worsened further after Lehman’s default in September 2008. As a consequence, total transactions among banks fell significantly. In Italy, in particular, they plummeted from €24bn. in 2006 to €4.8bn. at the end of 2009. At the same time, the cost of raising funds in the interbank market rose sharply: the Euribor-Eurepo spread, which was practically zero until August 2007, reached over 50 basis points for all maturities in the subsequent year. It then increased by five times after the Lehman crisis and remained well above 20 basis points in the following years. Two recent papers have exploited the collapse of the interbank market as a source of exogenous shock to credit supply. Iyer et al. (2013) used Spanish data to show that bank pre-crisis exposure to the interbank shock, as measured by the ratio of interbank liabilities to assets, was a significant predictor of a drop in credit granted during the crisis. Cingano et al. (2016) focus on CADS data for Italy to show that this drop had a significant negative effect on firms’ capital accumulation. These researchers reported results of several empirical tests showing that banks’ pre-crisis exposure was not correlated with their borrowers’ characteristics, such as investment opportunities and firm growth potential, thus making this variable particularly suitable to instrument the impact of credit supply on firms’ outcomes. We focus on the period 2007­2009, when credit dried up the most. Subsequently, ECB interventions partially offset the impact of the interbank market shock. Our measure of firm exposure to the credit supply tightening is the average 2006 interbank exposure of Italian banks at the firm level, using firms’ specific credit shares in 2006 as weights. Because firm exposure is time-invariant, we use cross-sectional variation. We include observations over a three-year window. Formally, for each firm i active in industry s and province p over the years t G [2007, 2009], we estimate the equation:

where ^j,t is firm idiosyncratic productivity, /NTBKi 2006 is the pre-crisis reliance on the interbank market, and Y is a set of provincexindustryxyear fixed effects. Results are shown in Table 6. Firms whose lenders were more reliant on the interbank market in 2006 had significantly lower revenue and value-added productivity growth during the credit crunch. This strengthens the causal interpretation of the relations between credit supply and productivity growth documented in section 5. A 1% increase in average bank dependence on the interbank market results in an approximately .05% decrease in average value-added productivity growth and an approximately .02% decrease in revenue productivity growth. Consequently, the same interbank shock which decreases credit growth by 1% also decreases value-added productivity of 0.25% and revenue productivity by one-tenth of a percent for the whole sample. These effects are between two and five times larger than the baseline estimate from Table 3, suggesting that accessing a reliable source of credit supply is particularly important during financial turmoil.

6.1 Placebo and robustness tests

Estimation of (12) provides evidence that firms hit harder by the credit crunch decrease their relative productivity. What if banks relying more heavily on the interbank market were just matched to worst borrowers? To remove this concern, we run equation (12) including only years before the freeze of the interbank market; that is, t G [2004, 2006]. Results, shown in columns (1)—(4) of Table 7, show that firms more exposed to the freeze of the interbank market did not have statistically different growth rates of productivity before the credit crunch. Additional results show that firms more exposed to the interbank shock were not more sensitive to business-cycle fluctuation before 2007. Details are in appendix D. We implement an additional placebo test. That is, we investigate the effect of a hypothetical freeze of the interbank market in 2003. For t G [2003, 2005] we estimate the model:

Columns (5)—(8) of Table 7 show that the placebo collapse is not a significant predictor of firms’ subsequent productivity growth.

7 Beyond Measurement: Channels

How does credit supply improve productivity? In this section, we investigate the relations between the credit supply shocks and several productivity-enhancing activities. As described in section 2, INVIND provides information about R&D investment, export, IT-adoption, and self-reported “obstacles to innovation” for a sample of Italian companies in services and manufacturing. Because both questions and respondents vary between waves, each specification of this section relies on a different sample. Furthermore, the sample size is much smaller than in the previous sections, limiting our ability to use our preferred specification.

In section 5.3, we show that credit supply shocks affect productivity immediately. We detect additional productivity growth for at least two years and higher productivity for at least four years. Unfortunately, our empirical framework is not fit to investigate the effect at a longer horizon. Some of the productivity-enhancing strategies studied in this section, such as IT-adoption or better man­agement practices, are likely to affect productivity as soon as they are implemented. Others, such as R&D, might take a few years to produce substantial improvement. Therefore, this section does not only explore the potential mechanisms behind the effect we measure in section 5, but also suggests that credit availability might lead to additional productivity gains in the long run.

7.1 IT-intensity of capital stock​​​​​​​

The speed of adoption of IT technologies caused large differences in productivity between US and European companies (Bloom et al. , 2012). According to Pellegrino & Zingales (2014), failure to take full advantage of the IT revolution is one of the main drivers of Italy’s low productivity growth. Data on personal computers used is available from INVIND for 1999-2001. Purchases of PCs are accounted as investments. Therefore, they enter the computation of capital stock. Slacker credit constraints might allow firms to stay closer to the technological frontier. By making more technolog­ical investments, unconstrained firms might have a “better” capital stock. Since researchers do not have detailed information on “quality” or “closeness to the frontier” of inputs, this quality is picked up by the productivity residual. To test this hypothesis, we measure the “IT-intensity” of firm capital stock as (log) number of PCs per 1,000 euros of capital.

Results are presented in column (1) of Table 8. Firms are more likely to increase the IT-intensity of their capital stock when they receive a positive credit  supply shock. This finding suggests  that financial frictions lower the quality of capital inputs used in production.

7.2 Innovation and exporting​​​​​​​

Patenting activities have been extensively used as a proxy for firm-level knowledge creation (see Bernstein (2015) and Kogan et al. (2017) for recent examples). We obtain information for patent applications for a large fraction of Italian companies from PatStat, as described in section 2. In our sample, patent applications became much less common during (and after) the credit crunch. The share of firms applying for at least one patent was approximately 2% between 2002 and 2007. It declined  to 1.5% in 2009 and went up to  a bit more than 1.6% in the following  two years. We observe approximately 5 patent applications per 100 firms per year before the Great Recession, but only around 3.4 in 2009. This pattern, of course, could be driven by lower demand and/or greater uncertainty. To investigate whether availability of credit has a causal impact on patent applications, we estimate the models:

Following the literature on R&D and patents, we impose a lag between the credit shock and patent applications. Results are reported in columns (2) and (3) of Table 8. Italian firms patent more when they have easier access to bank credit. Appendix D.3 uses the collapse of the interbank market to provide additional evidence on the causal effect of the tightening of credit constraints on innovation. The sensitivity of international trade to financial frictions has been studied by several authors (Manova, 2012). We use the INVIND survey to identify firms that export and that have positive R&D expen­ditures. We focus on the extensive margin and estimate two linear probability models:

 

where R&Dj,t and Export^ are dummy variables indicating whether firm i engages in R&D or exporting in year t. Results, presented in columns (4) and (5) of Table 8, show that there is a positive and statistically significant relation between credit supply and the propensity for these productivity-enhancing activities. This indicates that companies are more likely to start (and less likely to stop) conducting R&D and exporting when they have easier access to external finance.

Innovative effort is much broader than just formal R&D or IT-adoption. The 2011 survey wave investigates the main constraints to innovative effort. One question asks how important, on a four- item scale, the firm’s difficulties in collecting external funds were in limiting innovation (in 2010). We build the variable FinConi,2010, equal to 1 if and only if difficulty in getting external funds is reported to be “somehow important” or “very important” as an obstacle to innovation. Then, we estimate the linear probability model:

Results are presented in column (6) of Table 8, which documents that firms receiving positive credit supply shocks are less likely to consider external funds as a substantial obstacle to innova­tion. Since the question was asked for only one year of the survey, we cannot use panel variation. Nonetheless, this exercise is an indirect—yet insightful—test of the hypothesis that financial frictions dampen firms’ innovative efforts.

7.3 Management practices

Management matters for firm performance, as shown by Bloom et al. (2013) for India and by Giorcelli (2016) for Italy. We use credit supply shocks to investigate whether firms improve their management when facing slacker financial constraints. The direction of the relation is not obvious. Scarcity of resources might push firms to improve their internal organization. Conversely, improvement in management practices might require stable financial resources; for instance, to hire professional consulting services or to restructure a production facility. Bhattacharya et al. (2013) propose a model in which frictions distort optimal investment in managerial skills.

We obtain firm-level data on management practices from the World Management Survey (WMS). As can be read from the website, WMS “developed an in-depth survey methodology and constructed a robust measure of management practices in order to investigate and explain differences in man­agement practices across firms and countries in different sectors.” Information on data construction can be found in Bloom & Van Reenen (2007). They state that the “practice evaluation tool defines and scores from one (worst practice) to five (best practice) across eighteen key management practices used by industrial firm.” Merging WMS data on Italian companies by name, we obtain a sample of 183 observations. Because we have only one or two survey waves for each firm, we estimate the cross-sectional model:

7.4 Managerial inattention

 Entrepreneurs connected to lenders who contract their credit supply might need to spend more time and energy to establish new lending relations. Therefore, they might exert less effort in improving their firm’s productivity. As a colorful piece of anecdotal evidence to support this theory, the aunt of one of the authors was managing the family business during the credit crunch. When asked about the firm’s performance, she used to reply, “I barely have time to go to the factory, I spend most of my mornings at banks trying to get some money.” As an indirect test of this mechanism, appendix A.2 and the relative results in Table A.1 show that firms receiving positive credit supply shocks are less likely to try to establish new lending relations. A more direct and complete investigation of this hypothesis is left to future research.

8 Conclusion

To grow and thrive, firms need reliable access to external funding. In particular, this paper carefully documents that credit supply is an important determinant of improvement to a firm’s performance, both in the short run and the long run.

We therefore study the impact of banks’ credit supply on production for a large sample of Italian corporations. We exploit the universe of bank-firm credit relationship over the period 1997-2013 to estimate an additive growth rate model and we separate demand from supply shocks using firm-time and bank-time fixed effects. We improve on the literature by considering two important extensions to this framework. Then, we use the estimated bank-level supply shocks and the stickiness of lending relationship to build a measure of firm-specific shocks to credit supply. We document that firms connected to banks which are expanding their supply of credit acquire more inputs and produce higher output than their competitors. We show that the effect on output is stronger than the effect on inputs, suggesting that productivity is affected by credit availability.

We build a model of production with heterogeneous credit constraints in order to estimate an industry-specific production function and isolate firm idiosyncratic productivity dynamics. Then, we show that credit supply boosts productivity growth and that these effects are sizable, persistent, and robust. Moreover, they are stronger for smaller firms and for companies in sectors relying heavily on bank credit. Furthermore, we exploit the 2007-08 freeze of the interbank market as a natural experiment to support the causal interpretation of our estimates and show that they are not driven by the assortative matching of borrowers and lenders or by reverse causality. Our results imply that financial turmoil can have a persistent effect on aggregate output because it depresses firms’ TFP in the short and long run. Furthermore, our findings suggest that financial frictions are harmful beyond their detrimental effects on allocative efficiency.

We show that a negative credit supply shock produces stronger effects than a positive one of the same magnitude. This finding implies that it is not only the quantity of credit supply that matters, but also its stability.

Finally, we show that several productivity-enhancing activities, such as adoption of IT, sound management practices, export orientation, and innovation, are stimulated by credit availability. We also conjecture that a reduction of credit supply might force borrowers (notably, managers and entrepreneurs) to consume time and energy in order to establish connections with additional lenders. Consequently, they might exert less effort in improving business performance. We document that firms’ attempts to create new lending relationships are indeed more frequent when they experience negative credit shocks.

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