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Credit Constraints, Firm Investment and Growth: Evidence from Survey Data

We assess the impact of credit constraints on investment, inventories and other working capital and firm growth with a large panel of small and medium-sized enterprises from 12 European countries for the period 2014-2016. The data come from the Survey on the access to finance of enterprises (SAFE), a survey that is especially designed to analyse the problems in the access to external finance of European SMEs. The key identification challenge is a potential reverse-causality bias, as firms with poor investment and growth opportunities may have a higher probability of being credit constrained. We implement several strategies to overcome this obstacle: proxies for investment opportunities, lagged regressors, random effects and instrumental variables. Our findings suggest that credit constraints, both in bank financing and other financing (e.g. trade credit), have strong negative effects on investment in fixed assets, while the impact on firm growth and working capital is less robust.


Introduction
According to the Modigliani-Miller theorem (1958), under certain conditions, a firm's capital structure is irrelevant to its value. This implies that, in perfect capital markets, a firm's financing decisions are independent from its investment decisions. In this case, internal and external funds are perfect substitutes, and real firm decisions, motivated by the maximisation of shareholders' claims, are independent of financial factors such as internal liquidity, debt leverage or dividend payments. In practice, however, factors such as transaction costs, tax advantages, costs of financial distress, agency costs and asymmetric information lead to an imperfect substitutability between internal and external funds 2 , leading to the external finance premium. 3 In this context, financial constraints may have an important (negative) effect on real variables such as investment, working capital and firm growth, especially for firms with insufficient internal funds (cashflows and retained earnings).
The purpose of this research is to test this theoretical prediction. We do so with a panel of about 5,000 small and medium-sized enterprises (SMEs) from 12 European countries for the period 2014-2016. The firm-level data come from the Survey on the access to finance of enterprises (SAFE), a survey that is run jointly by the European Central Bank and the European Commission every six months since 2009. 4 The survey, initiated in the middle of the Great Recession, was especially designed to analyse the problems in the access to external finance faced by European SMEs, so it constitutes an ideal source of information about the credit constraints experienced by those firms.
In line with previous studies on the SAFE 5 , we develop several survey-based indicators of credit constraints, distinguishing between constraints in the access to bank finance (bank loans, bank overdrafts, credit lines) and in the access to other finance (trade credit, leasing, factoring, debt and equity securities, etc). While bank finance is the predominant source of external funds for SMEs in Europe, Casey and O'Toole (2014) find that bank-constrained SMEs substitute trade credit, informal lending and loans from other companies for bank credit. 6 Hence, it is crucial to control for all sources of external finance when assessing firms' financial constraints to avoid an omitted variable bias. Following the existing literature 7 , our measures of credit constraints are dummy variables that equal one if any of the following circumstances took place: a) a firm's application to external financing got rejected; b) a firm only received a limited part of what it applied for (i.e., quantity rationing); c) a firm refused the lender's proposal for external financing because the borrowing costs were too high (i.e., price rationing); d) a firm did not apply for external financing because it feared its application would be rejected (i.e., discouraged borrowers). Nevertheless, we check the robustness of our results by using an alternative measure of financial constraints based on firms' perceptions about access to finance.
Our goal is to identify the causal effect of credit constraints on investment, inventories and other working capital and firm growth. The key identification challenge we face is a potential reverse-causality bias, as we expect firms with poor investment/growth opportunities to have a higher probability of being credit constrained. We implement several strategies to overcome this obstacle. First, we proxy investment opportunities with a measure of enterprise-specific outlook 8 , as in Ferrando and Mulier (2015). We also control for traditional determinants of investment opportunities such as firm size and age (Petersen and Rajan, 1994) and include changes in firm's turnover as in empirical investment models based on the "acceleration principle" (Abel and Blanchard, 1986). 9 Second, in all our regressions we include countryindustry-time fixed effects to control for time-varying country-specific and industry-specific investment and growth opportunities. Third, to avoid the potential contemporaneous endogeneity between investment dynamics, firm growth and credit constraints, we exploit the panel nature of our data and include lagged values of the credit constraint dummies, as well as lagged values of the time-varying controls.
In robustness, we also carry out two other strategies. First, we control for firm-level unobserved heterogeneity with a random-effects ordered probit model, which may capture the time-invariant component of investment/growth opportunities and help us mitigate the omitted variable bias.
Second, as we cannot perfectly control for firms' investment and growth opportunities, we use an instrumental variable to isolate the exogenous part of credit constraints. The proposed instrument is a variable that measures the level of credit standards of each country in each round of the SAFE. The variable comes from the ECB's Bank Lending Survey (BLS), a survey that asks euro area banks about developments in their respective credit markets. Crucially, the BLS is especially designed to distinguish between supply and demand conditions in the euro area credit markets. Supply conditions are measured with credit standards, credit terms and conditions and the various factors that may have caused them to change. According to the BLS, "credit standards are the internal guidelines or loan approval criteria of a bank. They are established prior to the actual loan negotiation on the terms and conditions and the actual loan approval/rejection decision…" In other words, they are the prespecified criteria an individual or firm must meet to be eligible for certain loan, i.e., the ex-ante probability of granting a loan, keeping the borrower's creditworthiness fixed. Therefore, credit standards have little to do with demand factors such as the firm's investment and growth opportunities and they satisfy the exclusion restriction. In addition, tighter credit standards are likely to increase the probability of credit constraints, making them a relevant instrument.
Our main findings suggest that credit constraints, both in bank financing and other financing, have important effects in investment in fixed assets, inventories and other working capital and firm growth. According to our baseline estimates (with a pooled ordered probit model), a firm that is constrained in bank financing has 2.1 percentage point higher probability of decreasing investment and a 2.8 pp lower probability of increasing it. 10 Likewise, a firm that is constrained in other financing has 3.4 pp higher probability of decreasing investment and a 4.5 pp lower probability of increasing it. Notice that these effects are additive, implying that a firm that is constrained in all sorts of financing face a 5.5 pp greater likelihood of cutting down investment and a 7.3 pp lower likelihood of raising it. The effects on firm growth (measured in terms of employment) are of similar magnitude, but only credit constraints in bank financing have a significant effect. In particular, being bank-constrained raises 2.9 pp the probability of decreasing employment and reduces 4.1 pp the probability of increasing it. Regarding inventories and other working capital, it is interesting to notice that only credit constraints in other financing have a significant impact, probably capturing the crucial role of trade credit in customer-supplier relationships. Specifically, a firm that is constrained in other financing has 4.9 pp higher probability of decreasing working capital and a 5.7 pp lower probability of increasing it. Things change little when we add random effects to the model, and the results with an alternative measure of credit constraints confirm their importance. The IV estimations 10 One of the advantages of ordered probit models, relative to binary choice models such as probit or logit, is that we can allow for asymmetric effects, i.e., the effect on the probability of increasing investment and the effect on the probability of decreasing it do not necessarily have the same size.
suggest that overall credit constraints have strong negative effects on investment in fixed assets, while the impact on firm growth and working capital is less robust.
In addition, we analyse heterogeneous effects by computing average marginal effects for different types of firms (size, age, ownership structure). A remarkable result is that micro firms (less than 10 employees) and relatively young companies (less than 5 years old) are largely unaffected by credit constraints, probably because those firms rely more on internal funds (cashflows and retained earnings) to fund their investment projects, making them less sensitive to access to external funds. This result complements those of Beck et al. (2005), who find that small and medium-sized enterprises (SMEs) face greater financial, legal and corruption obstacles compared to large firms and the constraining impact of obstacles on firm growth is inversely related to firm size. However, Beck et al. (2005) compare SMEs with large firms, while we compare micro, small and medium-sized firms. 11 Hence, the effect of credit constraints on firm investment and growth may be a non-monotonic (concave) function of firm size. Moreover, the negative effect of credit constraints on firm outcomes seems to be mainly driven by the impact on family businesses and on sole traders, while most of the other ownership categories (e.g. listed companies, firms owned by other enterprises) are largely unaffected, which is consistent with asymmetric information problems.
The rest of the paper is organised as follows. Section 2 reviews related literature and explains this paper's contribution. Section 3 describes the sample and the construction of the variables used for the empirical analysis. Section 4 explains the econometric techniques and the identification strategy. Section 5 describes the baseline results. Section 6 displays several robustness tests. Finally, section 7 concludes. Some descriptive statistics, technical details and additional results are displayed in several appendices.

Related literature and contribution.
Most previous research on financial constraints is based on financial statement data. In this literature, the standard approach is to use indirect measures of financial constraints such as dividend pay-out behaviour, association with business groups, size, age, ownership form and credit ratings to test whether the sensitivity of investment to cashflows is greater in the group of firms that are more likely to be constrained. 12 For instance, the seminal work of Fazzari et al. (1988) classified US firms according to their dividend pay-out ratio. The intuition is that, if the cost disadvantage of external finance is small, retention practices should reveal little or nothing about investment, as firms will simply use external funds to smooth investment when internal finance fluctuates. By contrast, if the cost disadvantage is significant (i.e., large external finance premium) firms that retain and invest most of their income (i.e., those with a low pay-out ratio) may have no low-cost source of investment finance, and their investment should be driven by fluctuations in cash flows. Consistent with this hypothesis, the authors find that the investment of firms that exhaust all their internal finance is more sensitive to fluctuations in cash flow than that of high-dividend firms. Nevertheless, a standard criticism of the studies on investment-cash flow sensitivities is that liquidity proxies for other unobservable determinants of investment such as the profitability of investment. High 11 Beck et al. (2005) compare small (5-50 employees), medium-sized (51-500) and large firms (more than 500 employees), while we compare micro (1-9 employees), small (10-49 employees) and medium-sized firms (50-249 employees) and we do not have large firms in our sample.

12
See Schiantarelli (1996) for a review of this vast literature. liquidity signals that the firm has done well and is likely to continue doing well. Thus, more liquid firms have better investment opportunities, and it is not surprising that they tend to invest more. Although one can use the Tobin's q to control for investment opportunities, Tobin's q is difficult to measure in practice and may well differ from the marginal q firms use to make their investment decisions. In addition, this strand of the literature has been challenged by Kaplan and Zingales (1997), who provide theoretical reasons and empirical evidence that a greater sensitivity of investment to cash flow is not a reliable measure of financing constraints. 13 Given the limitations of those studies, there is a new emerging strand of the literature that attempts to assess the impact of financial constraints on real variables using survey data. The key idea is to obtain direct measures of financial constraints by asking firms about potential problems in their access to credit markets. Campello et al. (2010) do so with two samples, a cross-section of 1,050 very large corporations 14 from US, Europe and Asia in the fourth quarter of 2008 and a smaller rotating panel of US companies in the 2007Q3-2008Q4 period. Using the first dataset, they find that constrained firms planned, on average, deeper cuts in technology expenditures, capital expenditures, marketing expenditures, employment, cash holdings and dividend payments than unconstrained firms. In addition, recognising that comparisons of means may be confounded by systematic differences between constrained and unconstrained firms in other dimensions, they undertake matching techniques on several observable characteristics with their sample of US companies, finding significant differences between the two groups. Nevertheless, a limitation of their identification strategy is the potential endogeneity of financial constraints. If a firm's poor performance (e.g. lower spending in technology or capital) increases the likelihood of being credit constrained, then the matching estimators will fail to deliver the causal impact of financial constraints on investment and growth. 15 Another study, very much in the spirit of this paper, is Ferrando and Mulier (2015), in which they analyse the effect of being a discouraged borrower (i.e., firms that do not apply for a bank loan because they fear that their application will be rejected) on firm investment and growth. To do so they use a unique database that matches firms' answers to the SAFE with their financial statements for 9 euro area countries from the second quarter of 2010 until the first quarter of 2014. To take into account the endogeneity between discouragement and investment/growth, they use two-stage least squares and instrument their dummy for discouraged borrowers with a firm-level financial constraints indicator, namely a dummy that equals 1 if the firm considers access to finance as the most pressing problem. However, such an instrument is likely to be invalid if lenders observe a firm's lack of investment/growth 13 Kaplan and Zingales (1997) undertake an in-depth analysis of the 49 low dividend firms that Fazzari et al. (1988) identify as financially constrained by examining managers' views on their firms' access to credit (gleaned from managers' statements filed in corporate 10-Ks), complemented with some quantitative data and public news. On this basis, they rank the extent to which the firms are financially constrained. Strikingly, they find that those firms classified as less financially constrained exhibit a significantly greater investment-cash flow sensitivity than those firms classified as more financially constrained.
14 For instance, they categorise as "small" those firms with total gross sales amounting to less than $1 billion or with less than 500 employees. By contrast, according to the European Commission, small and medium sized firms are those with turnover less than $50 million and less than 250 employees.

15
This is acknowledged by the authors: "Yet another concern is whether uncontrolled firm heterogeneity could confound our inferences. Consider, for example, a company that performs poorly even before the crisis. It would not be surprising to find that this firm might both do worse during the crisis (e.g., invest less) and find less available credit" (page 471). opportunities and in turn decide to restrain credit, making access to credit the firm's most pressing problem. To put it differently, their endogenous regressor is a financial constraints indicator, "discouraged borrowers", and their instrumental variable is another financial constraints indicator, "access to finance as most pressing problem", which is likely to be endogenous as well. Nevertheless, their estimates suggest that there is a negative and strong correlation between credit constraints, firm investment and growth.
In addition, Beck et al. (2005) use data on 4,000 firms from 54 countries from a cross-sectional survey conducted by the World Bank in 1999-2000, the World Business Environment Survey (WEBS), to analyse the effect of financial, legal and corruption obstacles on firm growth. For instance, in the case of financial obstacles, the survey includes questions that require, as an answer, a rating from 1 (no obstacle) to 4 (major obstacle) on factors such as collateral requirements, bank paperwork and bureaucracy, high interest rates and access to long-term loans. These are perceived financial obstacles, rather than actual financing constraints, as the formulation of the questions does not allow knowing if firms were denied credit. The authors find a negative correlation between those obstacles and firm growth. They also find that the negative impact of the obstacles on firm growth is a decreasing function of firm size (i.e., the smallest firms are consistently the most adversely affected by all obstacles) and it is inversely related to institutional development (i.e., financial and institutional development weakens the constraining effects of financial, legal and corruption obstacles.) Nevertheless, the lack of firm-level measures of investment opportunities and the potential endogeneity of the firmlevel obstacles (firms that are not growing because of internal problems systematically shift blame to the legal and financial institutions and report high obstacles) cast some doubts on the causal interpretation of their estimates. 16 In a similar fashion, Coluzzi et al. (2015) study the impact of financial obstacles on firm growth in five euro area countries 17 using the WEBS and the Amadeus database. Identification of the causal impact of financial obstacles on firm growth relies on the estimation via the GMM-system estimator, in which all the regressors are instrumented with their lagged values. While the estimations deliver a negative coefficient on the financial obstacles variable in four out of the five countries, the Sargan-Hansen test rejects the null hypothesis of validity of overidentifiying restrictions in most cases, casting some doubts on the results.
Our paper contributes to the existing literature on the real effects of credit constraints in several ways. 18 First, it extends the work of Ferrando and Mulier (2015) on discouraged borrowers to both "formal" and "informal" credit constraints (discouragement, quantity rationing, price rationing, rejected applications) and assesses the role of all sources of financing, not only bank loans, in shaping business decisions. It also covers a larger number of countries and analyses the recovery period of the European economy (2014-2016), unlike previous studies that have focused in the last recession. This is particularly interesting as the Eurozone prepares itself for monetary policy normalisation, which could influence on firms' 16 Aware of the potential revere-causality bias, the authors carry out some sensitivity analyses in which the financial, legal and corruption obstacles are instrumented by measures of institutional development, namely, the development of the financial system, the legal system and the country's level of corruption. It is not clear, however, that the proposed instruments satisfy the exclusion restriction, as in previous regressions of firm growth on obstacles and institutions they are used as explanatory variables. 17 France, Germany, Italy, Portugal, Spain.

18
In addition, there are several papers that use quasi-experimental techniques to estimate the real effects of credit supply shocks. See, inter alia, Jiménez et al. (2017), Alfaro et al. (2016), Greenstone et al. (2014), Chodorow-Reich (2014), Acharya et.al (2016), Balduzzi et al. (2016). See also Buca and Vermeulen (2015) for the effect of bank credit tightening on firm investment. investment via the so-called balance sheet channel (Bernanke and Gertler, 1995) and the external finance premium paid by credit constrained firms. 19 It also looks at the impact of financial constraints on inventories and other working capital, an aspect that has traditionally been overlooked in the literature, with few exceptions (Fazzari and Petersen, 1993). Finally, it attempts to establish a causal link between credit constraints and firm investment, inventories and growth by exploiting the panel nature of the data and by making use of an instrumental variable to isolate the exogenous part of credit constraints.

Data description and construction of variables.
The data source for our analysis is the firm-level Survey on the Access to Finance of Enterprises (SAFE), which is run jointly by the ECB and the European Commission since 2009. The sample contains only non-financial firms and excludes firms in agriculture, public administration and financial services. Some of the firms are interviewed only once in the survey but there is a rotating panel of enterprises that are re-surveyed in subsequent rounds, which is the dataset we use for our analyses. We do not use answers by large firms 20 because the anonymised micro dataset does not provide information on the sector of activity of those companies to protect the confidentiality of the answers, and the industry of activity is an important control variable in our regression analysis. We also limit our sample to the rounds 11 to 15 of SAFE (from April-September 2014 to April-September 2016) because of the availability of some key variables. 21 After applying these filters, we end up with a sample of 7,162 non-missing observations 22 corresponding to 4,880 small and medium-sized enterprises (SMEs) from 12 European countries. 23 Most of the information of the SAFE is qualitative, implying that most of the variables in the sample are categorical. Table 1 lists the names of the variables and the values they can take. A first set of variables contains information on the general characteristics of the firms such as country, industry, size (measured by the number of employees 24 or by turnover volume), age (in intervals of years), legal form (whether the firm is an autonomous enterprise or a subsidiary/branch of another enterprise), ownership structure (whether the firm is owned by a single natural person, by a family, by public shareholders, etc) and export activity. 25

19
The balance sheet channel theorises that the size of the external finance premium is inversely related to the borrower's net worth (liquid assets plus the collateral value of illiquid assets). Hence, an increase in the interest rate will work not only through the traditional impact on the user cost of capital, but also through the adverse impact on the present value of collateralizable net worth, leading to a widening of the wedge between the cost of external and internal finance and, therefore, to a reduction in investment and production.

20
Defined as those companies with 250 employees or more.

21
The SAFE questionnaire has been amended in several occasions, which provokes breaks in some series. In addition, some of the questions, which belong to a more comprehensive questionnaire, are only asked every one or two years in the rounds that are run in cooperation with the European Commission.

22
The actual number of observations used in the estimations varies according to the dependent variable and the selected measure of credit constraints. Here we present descriptive statistics for the sample used for the baseline regressions of investment growth, as presented in Table 6 23 Austria, Belgium, Germany, Spain, Finland, France, Greece, Ireland, Italy, Netherlands, Portugal, Slovakia.

25
A firm is an exporter if any percentage of the company's turnover is accounted for by exports of goods and services.
A second set of variables comprises several measures of credit constraints in bank financing (bank loans and credit lines) and in trade credit and other financing (equity and debt securities, leasing, factoring, intercompany loans, etc). Our preferred measures are the socalled wide indicators of credit constraints, which combine formal and informal credit constraints, a strategy that is standard in the literature since the seminal work of Jappelli (1990). Following the existing literature 26 , those variables equal one if, for each type of financing, any of the following circumstances took place: a) a firm's application to external financing got rejected; b) a firm only received a limited part (i.e., less than 75%) of what it applied for (i.e., quantity rationing); c) a firm refused the lender's proposal for external financing because the borrowing costs were too high (i.e., price rationing); d) a firm did not apply for external financing because it feared its application would be rejected (i.e., discouraged borrowers). 27 As in Ferrando et al. (2017) and Casey and O'Toole (2014), we build a single credit constraints indicator for bank loans and credit lines (cc_bank), assuming that a firm is constrained in bank financing if it is constrained in at least one of the two. 28 Hence, we are implicitly assuming that bank loans and credit lines are imperfect substitutes. This seems a plausible assumption, as loans are more likely to be used to fund large investments in fixed assets and credit lines are more commonly used to finance working capital. In analogous fashion, we build a single credit constraints indicator for trade credit and other financing (cc_other), assuming that a firm is constrained in non-bank financing if it is constrained in at least one of the two. In some of our analyses we merge cc_bank and cc_other into a single variable, cc_all, which equals 1 if the firm is constrained in at least one of the two financing sources (i.e., cc_bank=1 and/or cc_other=1) and 0 if the firm is constrained in none of them (i.e., cc_bank=cc_other=0).
In addition, we carry out robustness analyses with an alternative measure of credit constraints, problem_access_finance, which is a variable that indicates how important the problem "access to finance" is to the firm. In particular, the firm is asked to assess the importance of a series of problems (finding customers, competition, access to finance, costs of production or labour, availability of skilled staff, regulation, other) using a scale of 1-10, where 1 means it is not at all important and 10 means it is extremely important. Hence, this variable captures "perceived financing constraints", while the wide indicator of credit constraints measures "actual financing constraints", as distinguished by Ferrando and Mulier (2013).
A third set of variables indicates changes in the economic and financial situation of the firm. In particular, firms must answer whether a set of indicators, such as investment, working capital, employment, interest expenses or debt to assets have decreased, remained unchanged or increased over the last six months before the survey. 29 Finally, a fourth set of variables 26 See, for instance, Ferrando et al. (2017), Ferrando and Mulier (2013) and Artola and Genre (2011). 27 The indicator is based on questions Q7A and Q7B of SAFE. Those questions are asked only to those firms that consider each source of financing (e.g. bank loans) relevant, as determined in question Q4. This is the main reason why there is a non-negligible number of missing values in the variable. 28 We follow this strategy to maximise sample size. For instance, if a firm is constrained (unconstrained) in bank loans and has missing information on credit lines, we assume the firm is constrained (unconstrained) in bank financing.

29
The question of SAFE is Q2."Have the following company indicators decreased, remained unchanged or increased over the past six months?" Answers on some indicators, such as investment, working indicates whether some factors, such as the enterprise-specific outlook, the firm's own capital and the firm's credit history, have improved, remained unchanged or deteriorated. Table A1 of Appendix A shows the breakdown of observations by country. It can be seen that the survey contains more observations for the larger economies in order to be sufficiently representative for these countries. France, Germany, Italy and Spain each account of about 10-15% of the firms in the sample. Around 50% of observations belong to the "vulnerable countries" 30 , i.e., the euro area countries at the epicentre of the sovereign debt crisis (2009)(2010)(2011)(2012). Table A2 of Appendix A shows the breakdown of observations by the main firm characteristics. Around one third of the observations belong to the industry sector 31 , around ten percent to the construction sector, one fourth to wholesale or retail trade and 30% to the rest of services. Micro, small and medium firms each account for around one third of the sample. Most firms are more than 10 years old (85%), autonomous enterprises (89%) and owned by a family (55%) or sole traders (30%). Nearly half of them are exporters. Table 2 shows weighted descriptive statistics, constructed with sampling weights 32 , for the dependent variables of the analysis, the measures of credit constraints and the rest of controls. Concerning the dependent variables, a significant proportion of the firms report no changes in investment, working capital and employment, and the percentage of firms that report an increase in those variables is higher than the percentage that report a decrease. With respect to the indicators of credit constraints, sixteen per cent of firms are constrained in bank finance (cc_bank=1), ten per cent are constrained in trade credit or other financing (cc_other=1) and nineteen per cent are constrained in some source of financing (cc_all=1). The average value of problem_access_finance is 5.7 and its standard deviation is 2.9, which means that the average firm considers access to finance to be a relatively important problem. Table 3 shows pairwise correlations among the different measures of credit constraints. As the correlations among the wide indicators (cc_bank, cc_other, cc_all) and the measure of perceived financial constraints (problem_access_finance) are moderate, the analysis can benefit from using different measures of credit constraints. In addition, the relatively high correlation between cc_bank and cc_other (about 0.5) highlights the importance of controlling for credit constraints in trade credit and other financing when assessing the impact of credit constraints in bank finance on investment, working capital and firm growth. In similar fashion, Table 4 shows the value of the Cramer's V, which is a measure of association between two categorical variables that ranges between 0 and 1, for each pair of dependent variables. All values are between 0,2 and 0,3, indicating a moderate association among the variables.
To inspect a possible link between financial constraints and the dependent variables of our analysis (investment, working capital and employment), Figure 1 shows the distribution of those variables conditional on the values of cc_bank, as well as the statistic and p-value of the Pearson's chi-squared test of independence and the value of the Cramer's V. The picture that emerges is quite similar in the three variables. In each case, we can observe that the percentage of firms that report a decrease in the variable is substantially larger (at least 10 capital and number of employees are only available since round 11 of SAFE, when the question was extended.

30
Vulnerable countries are Portugal, Italy, Ireland, Greece, Spain, Slovenia and Cyprus.

31
Industry includes manufacturing, mining and electricity, gas and water supply.

32
As the sample is stratified by country, enterprise size class and economic activity, we use sampling weights in all our statistical analyses. The weights restore the proportions of the economic weight (in terms of number of employees) of each size class, economic activity and country. percentage points) in the group of financially constrained firms (cc_bank=1), while the percentage of firms that report an increase in the variable is substantially larger (at least 5 percentage points) in the group of firms without credit constraints (cc_bank=0). The percentage of firms whose investment remained unchanged is also slightly lower in the group of constrained firms. These differences are also statistically significant, as we reject the null hypothesis of independence in the Pearson's chi-squared test for a 1% significance level. The values of the Cramer's V, between 0.1 and 0.2, also suggest meaningful relationships. Same qualitative results are found for credit constraints in other financing ( Figure 2) although the values of the Cramer's V are slightly lower, suggesting somewhat weaker relationships.
Finally, constrained and unconstrained firms may differ in their main characteristics. This is inspected in Table 5 for the case of credit constraints in bank financing. Consistent with the literature that finds a negative relationship between the probability of experiencing financial constraints and size 33 , an 18% of micro firms are constrained, while this figure goes down to 14% and 9% in the case of small and medium firms, respectively. The same is true when firms are categorised in terms of their turnover. Likewise, there is a monotonic decreasing relation between the proportion of credit constraints and firm age, with mature firms (10 or more years) being 5 pp less likely to experience constraints than new ones (less than 2 years), in line with previous studies. 34 Also consistent with the literature that suggests that belonging to a business group relaxes financial constraints 35 , the proportion of constrained firms that are subsidiary or branches is significantly lower (10%) than that of autonomous enterprises (15%). Ownership structure also matters, as sole traders and family businesses are more likely (15% in both cases) to being constrained than publicly-listed firms (9%). There is also a significant proportion of constrained firms among those owned by venture capital enterprises (16%), as venture capital tends to fund new and risky projects for which bank finance is often not available. Exporting firms are less likely to be financially constrained than non-exporting (although the difference is quite small) because the former tend to be more competitive and productive (Correa-López and Doménech, 2012). Finally, the proportion of credit constrained firms is higher in vulnerable (19%) than in less vulnerable countries (12%). All these differences highlight the importance of controlling for those factors when attempting to establish a causal link between financial constraints and firms' real outcomes.

Empirical methods and identification strategy
To identify the causal impact of credit constraints on investment and working capital an ordered probit model has been used. For brevity of exposition, let us focus on the dependent variable investment growth, ∆". The order probit model is specified in terms of a continuous latent variable, latent investment growth ∆" * : where i is firm, j is industry, c is country, t is wave, ) $ * is a vector of variables that includes a set of credit constraints and firm-level controls, -%&' are country-industry-time fixed effects and / $%&' is a disturbance that follows a N(0,1). The sign of the regression parameters 0 can be immediately interpreted as determining whether the latent variable " $%&' * increases with the regressor or not.
Observed investment growth ∆", as reported to the SAFE, is then related to latent investment growth ∆" * in the following way: where the parameters = > , = L , = R are thresholds to be jointly estimated with the slope parameters. See Appendix A for details about the estimation.
Our goal is to identify the causal effect of credit constraints on firm investment, working capital and firm growth. The key identification challenge we face is a potential reversecausality bias, as we expect firms with poor investment/growth opportunities to have a higher probability of being credit constrained. To tackle this problem our identification strategy relies in the use of a comprehensive set of fixed effects, together with many firm-level covariates, to control for firms' investment opportunities.
Hence, in all our regressions we include country-industry-time fixed effects (i.e., a dummy for each country-industry-wave combination) to eliminate variation in the dependent variable that is specific to a particular country in a particular industry during a particular period of time (e.g., construction in Spain during the housing bust). This large set of dummies controls for time-varying country-specific and industry-specific investment and growth opportunities.
Our favourite measure of investment opportunities is enterprise outlook, an indicator for changes in the enterprise-specific outlook, also used by Ferrando and Mulier (2015). In particular, the firm is asked to assess the evolution of its own outlook, with respect to its sales and profitability or business plan, i.e., whether it has improved, remained unchanged or deteriorated over the past six months. We also include an indicator for changes in firm's turnover (i.e., whether it has increased, remained unchanged or decreased over the past six months) as a proxy for growth opportunities, as in Gomes (2001). 36 Regarding the rest of firmlevel controls, size and age, together with the firm's industry, are traditional determinants of investment opportunities (Petersen and Rajan, 1994). In addition, they are correlated with credit constraints, as discussed in the previous section. We also control for the degree of autonomy of the firm (whether the firm is an autonomous enterprise or a subsidiary/branch of another enterprise), and include ownership structure (whether the firm is owned by a single natural person, by a family, by public shareholders, etc) in our regressions, as those factors are likely to influence investment decisions, and we include a dummy that equals 1 if the firm is an exporter. In addition, there is a vast literature that studies the impact of firms' financial position on investment and employment decisions. 37 To control for it, we follow Casey and 36 The growth rate of sales is also used in empirical investment models based on the "acceleration principle", which links the demand for capital goods to the change in a firm's output or sales (see, inter alia, Abel and Blanchard, 1986). 37 See Herranz and Martínez-Carrascal (2017) for a review and an application to the Spanish economy.
O'Toole (2014) and include indicators for changes in firms' profits, capital positions, debt to asset ratios, interest expenses and credit histories. We also include indicators for changes in labour costs and other costs as measures of trading quality and production risk.
An identification challenge is the potential contemporaneous endogeneity between investment dynamics, firm growth and credit constraints. For example, within the six month windows that we observe in our data, a firm may experience a negative shock in its investment/growth opportunities (for instance, the entry into the market of a new competitor or some adverse regulatory change) and lenders, observing such a shock, may decide to cut their supply of credit to the firm. In this case, the shock in investment opportunities would be driving the probability of being credit constrained and therefore the relationship would be endogenous. To identify the causal relationship correctly and rule out any such endogeneity, we exploit the panel nature of our data and include lagged values of the credit constraint dummies, as well as lagged values of the time-varying controls. In robustness, we also control for firm-level unobserved heterogeneity with a random-effects ordered probit model. This technique may capture the time-invariant component of investment/growth opportunities (i.e., firms with high/low investment opportunities) and help us mitigate the omitted variable bias.
Nevertheless, a potential criticism to the previous identification strategy is that we cannot perfectly control for firms' investment and growth opportunities, implying that the error term may be correlated with the credit constraint dummies. Hence, in robustness, we use an instrumental variable to isolate the exogenous part of the key regressors. The proposed instrument, credit standards, is an ordinal variable that measures the level of credit standards of each country in each round of the SAFE. The variable comes from the ECB's Bank Lending Survey (BLS), a quarterly survey that asks euro area banks about developments in their respective credit markets since 2003.
Crucially, the BLS is especially designed to distinguish between supply and demand conditions in the euro area credit markets. Supply conditions are measured with credit standards, credit terms and conditions and the various factors that may have caused them to change. According to the BLS, "credit standards are the internal guidelines or loan approval criteria of a bank. They are established prior to the actual loan negotiation on the terms and conditions and the actual loan approval/rejection decision… Credit standards specify the required borrower characteristics (e.g. balance sheet conditions, income situation, age, employment status) under which a loan can be obtained…" In other words, they are the prespecified criteria an individual or firm must meet to be eligible for certain loan, i.e., the ex-ante probability of granting a loan, keeping the borrower's creditworthiness fixed. Hence, only supply factors can change them: "… Credit standards may change owing to changes in the bank's cost of funds and balance sheet situation, changes in competition, changes in the bank's risk perception, changes in the bank's risk tolerance or regulatory changes, for instance." 38 Therefore, credit 38 The complete definition is the following. "Credit standards are the internal guidelines or loan approval criteria of a bank. They are established prior to the actual loan negotiation on the terms and conditions and the actual loan approval/rejection decision. They define the types of loan a bank considers desirable and undesirable, the designated sectoral or geographic priorities, the collateral deemed acceptable and unacceptable, etc. Credit standards specify the required borrower characteristics (e.g. balance sheet conditions, income situation, age, employment status) under which a loan can be obtained. In the survey, both changes in written loan policies and their application should be considered. Credit standards may change owing to changes in the bank's cost of funds and balance sheet situation, changes standards have little to do with demand factors such as the firm's investment and growth opportunities and they satisfy the exclusion restriction. In addition, tighter credit standards are likely to increase the probability of credit constraints, making them a relevant instrument, as the first-stage will corroborate.
Finally, we use cluster-robust standard errors to allow for potential heteroscedasticity and serial correlation across groups in the error structure. The selection of the clustering groups is specific to the particular regression undertaken and is indicated in the regression output.

Overall effects
Tables 6-8 display the estimation of pooled ordered probit models in which the dependent variables are, respectively, investment growth, working capital growth and employment growth. Column (1) shows the regression coefficients and columns (2)-(4) the marginal effects of the measures of credit constraints (cc_bank and cc_other) on the probability of each alternative (decrease, remain unchanged, increase). In terms of the base categories for the firm controls, micro firms with turnover less than €500,000 is the omitted category for the size dummies, more than 10 years for the age dummies, subsidiary/branch for legal form and publicly-listed firms for ownership structure. As in Casey and O'Toole (2014), standard errors are clustered at the country-wave level to allow for heteroscedasticity and serial correlation of the errors across firms in the same country in a given wave of the SAFE. 39 Let us start with Table 6. In column (1) the sign of the regression parameters indicates whether the latent variable increases or decreases with each regressor. Credit constraints in bank financing (cc_bank) and credit constraints in other financing (cc_other) are negatively correlated with latent investment growth. Regarding the controls, small and medium firms invest more than micro firms, relatively mature firms (between 5 and 10 years) invest more than rest of firms and sole traders invest more than the rest. Interestingly, firms whose outlook deteriorated or remained unchanged invest less than those whose outlook improved, confirming that the enterprise-specific outlook is a good proxy for investment opportunities.
In order to ascertain the size of these effects, we turn our attention to the marginal effects in columns (2)-(4). A credit constrained firm in bank financing (cc_bank=1) has a 2.1 percentage point (pp) larger probability of decreasing investment (column (2)) and a 2.8 pp lower probability of increasing investment (column (4)). The marginal effects of cc_other are somewhat larger. Specifically, a credit constrained firm in other financing (cc_other=1) has a 3.4 percentage point (pp) larger probability of decreasing investment (column (2)) and a 4.5 pp lower probability of increasing investment (column (4)). Notice that these effects are additive, implying that a firm that is constrained in all sorts of financing (cc_bank= cc_other=1) faces a 5.5 pp greater likelihood of cutting down investment and a 7.3 pp lower likelihood of raising it.
in competition, changes in the bank's risk perception, changes in the bank's risk tolerance or regulatory changes, for instance." 39 Alternatively, we have computed standard errors that are two-way clustered at the firm and at the country-wave level, to allow for correlation of the error within firms across years and across firms in the same country in a given wave. The results, available upon request, are very similar.
Interestingly, column (1) of Table 7 reveals that cc_bank has no significant correlation with latent working capital growth, while cc_other is negatively correlated with that variable, which may be explained by the crucial role of trade credit in customer-supplier relationships.
Regarding the controls, firms with turnover greater than € 2 million experience larger increases in working capital than the rest of companies, relatively mature firms (between 5 and 10 years) experience larger increases in working capital than the rest and autonomous enterprises invest more in liquid assets than subsidiaries or branches, probably because of a precautionary savings strategy, as they cannot rely on intra-group financing in the event of additional liquidity needs. In addition, firms whose outlook deteriorated or remained unchanged invest less in working capital than those whose outlook improved. According to columns (2) and (4), a credit constrained firm in other financing (cc_other=1) has a 4.9 pp larger probability of decreasing working capital and a 5.7 pp lower probability of increasing it, while the marginal effects of cc_bank are not statistically different from zero.
A similar picture emerges when we examine the effect of credit constrains on firm growth (Table 8). According to Table 7, a firm that is constrained in bank financing has a 2.9 pp larger probability of decreasing employment and a 4.1 lower probability of increasing it, while the marginal effects of cc_other are not statistically different from zero. Regarding the controls, small and medium firms experience higher growth than micro firms and middle-aged firms (between 2 and 10 years) experience higher growth than the rest. A deteriorated business outlook has a negative impact on firm growth.

Heterogeneous effects
Given that SMEs are a very heterogeneous group of firms, and to provide a more granular insight into the real effect of credit constraints, we have calculated the marginal effects of the credit constraints variables using the previous model for different values of some firm characteristics. Beck et al. (2006) find that, for SMEs, firm age, size and ownership are important determinants of firm financing constraints. We therefore estimate the marginal effects for different firm groups using these key characteristics. 40 In particular, we interact the credit constraint variables with the size dummies (micro, small and medium, the age dummies (10 years or more, 5 to 9 years, 2 to 4 years, less than 2 years) and the ownership dummies (listed company, family business, firm owned by another enterprise 41 , sole trader, other). We then regress each dependent variable (investment, working capital, employment) on the control variables, the size/age/ownership dummies and the interactions between those variables and the measures of credit constraints. The marginal effects of the interactions may differ because the impact of credit constraints can depend on firm size, age or ownership structure. The vector of firm-level controls and country-industry-time dummies is the same as in previous estimations.
In Table 9 we present the marginal effects of the interactions between the size dummies and the credit constraint measures on the probability that the dependent variable increases. According to column (1), the impact of credit constraints on investment is very limited in the 40 Casey and O'Toole (2014), in their study of the effects of credit constraints on alternative finance usage with the SAFE database, also compute marginal effects for those variables. 41 Due to the low number of observations, the category "owned by venture capital enterprise or business angel" has been merged with the category "owned by another enterprise". case of micro firms (it is only significant at 10% in other financing). This may indicate that micro firms rely more on internal funds (cashflows and retained earnings) to fund their investment projects, making them less sensitive to access to external funds. The effect is higher and more significant in small and medium firms. For instance, a small (medium) firm that is constrained in bank financing (other financing) has a 5.9 pp (6.5 pp) lower probability of increasing investment than an otherwise similar firm. In the case of working capital (column (2)), the impact is only significant in the case of small firms and constraints in other financing: a small firm that is credit constrained in other financing has a 9 pp lower probability of increasing working capital than an otherwise similar firm. In the case of employment (column (3)), the effect of credit constraints in bank financing is only marginally significant, while the impact of constraints in other financing is large and significant only for small firms: a small bank constrained firm has an 8.3 pp lower probability of increasing employment than an otherwise similar firm. More generally, the conclusion that can be drawn from Table 9 is that credit constraints matter more for small and medium enterprises than for micro firms. Table 10 shows the marginal effects of the interactions between the age dummies and the credit constraint measures on the probability that the dependent variable increases. According to column (1), the negative effect of credit constraints on investment is mainly due to relatively old firms (5 years or more). For instance, for those firms that are 10 years or more, being bank constrained reduces the probability of increasing investment by 4.5 pp. The impact of credit constraints on working capital (column (2)) is only significant in the case of old firms (10 years or more): credit constraints in other financing reduce the probability of increasing working capital by 6.3 pp. Similarly, the effect of constraints on employment (column (3)) is only significant in relatively old firms (5 years or more). For example, the presence of bank constraints reduces the likelihood of increasing employment by 5 pp in the case of old firms (10 years or more). The upshot of this discussion is that relatively young firms (less than 5 years old) do not seem to be affected by credit constraints, probably because, as in the case of micro firms, they mainly rely on internal funds for their investment and employment decisions.
Finally, Table 11 shows the marginal effects of the interactions between the ownership dummies and the credit constraint measures on the probability that the dependent variable increases. The negative effect of credit constraints on firm outcomes seems to be mainly driven by the impact on family businesses and on sole traders, while most of the other ownership categories (e.g. listed companies, firms owned by other enterprises) are largely unaffected, which is consistent with asymmetric information problems. For instance, the presence of credit constraints in other financing reduces by 4.8 pp the probability of increasing investment and by 4.6 pp the probability of increasing inventories and other working capital in family-owned businesses, while the likelihood of increasing employment is reduced by 3.5 pp when those firms face credit constrains in bank financing.

Random effects
We may also control for firm-level unobserved heterogeneity with a random-effects 42 ordered probit model. This technique may capture the time-invariant component of investment/growth opportunities (i.e., firms with high/low investment opportunities) and help us mitigate the omitted variable bias. Tables 12-14 present the estimations of these models, where the same set of firm-level controls and fixed effects are included. Comparing Tables 6 and 12 (impact on investment), the coefficient on cc_bank and cc_other are still statistically significant, but the marginal effects are somewhat smaller than those estimated in the baseline specification. When comparing Tables 7 and 13 (impact on working capital), we see that the coefficients and marginal effects are remarkably similar. Finally, the comparison of Tables 8 and 14 (impact on employment growth) shows that the coefficient of cc_bank keeps their statistical significance and that the associated marginal effects are of similar magnitude, while those of cc_other now become significant. Finally, notice that the estimated panel-level variances (i.e., the variances of the random effects) in Tables 12-14 are large and statistically significant, which suggests that there is enough variability between firms to model a randomeffects ordered probit, rather than a pooled ordered probit.

Alternative measures of credit constraints
So far, in order to maximise sample size, our measures of credit constraints have been dummy variables that equal 1 for firms that are rejected, quantity rationed, price rationed or discouraged from applying (constrained firms), and equal 0 for the rest of firms (unconstrained firms). However, the latter is a heterogeneous group that comprises both firms that have successfully applied for a loan and firms that have not applied because they do not need it. In particular, firms may not apply for a loan because they have sufficient internal funds and because they do not have attractive investment opportunities. 43 As our aim is to establish a meaningful relationship between access to external finance, investment and growth, in robustness we only keep in the group of unconstrained firms those that have actually obtained a loan.
The results, presented in Tables 15-17, are very interesting when compared with the baseline results (Tables 6-8). The coefficients on cc_bank are no longer significant, while the coefficients and marginal effects of cc_other are always significant and larger than in the baseline regressions. For instance, according to Table 15, a firm that is constrained in other financing (cc_other=1) has a 4.3 percentage point (pp) larger probability of decreasing investment (column (2)) and 5.2 pp lower probability of increasing it (column (4)), effects somewhat larger than those reported in Table 6. According to Table 16, a firm that is constrained in other financing has a 5.7 percentage point (pp) larger probability of decreasing working capital (column (2)) and a 5.7 pp lower probability of increasing it (column (4)), effects slightly larger than those reported in Table 7. Finally, according to Table 17, a firm that is constrained in other financing has a 6.3 percentage point (pp) larger probability of reducing employment (column (2)) and 7.4 pp lower probability of increasing it (column (4)), while previously those effects were not significant (Table 8). More generally, the results corroborate the negative impact of credit constraints on investment, inventories and other working capital and employment dynamics.
In addition, we use an alternative measure of credit constraints, problem_access_finance, a variable that indicates how important the problem "access to finance" is to the firm. In particular, the firm is asked to assess the importance of a series of problems (finding customers, competition, access to finance, costs of production or labour, availability of skilled staff, regulation, other) using a scale of 1-10, where 1 means it is not at all important and 10 means it is extremely important. 44 Table 18-20 reports the estimation of ordered probit models in which problem_access_finance is the regressor of interest. In the three tables the coefficients and marginal effects are significant at the 1% level, although the overall impact is somewhat smaller than with the other measures. To assess the economic significance of the effect, let us consider that a constrained firm is the one whose value for problem_access_finance is the 75 th percentile (8) while an unconstrained firm is the one with value equal to the 25 th percentile (3), so that the size of the effect is the marginal effect times the interquartile range (5). Hence, a constrained firm has a 1.5 pp higher probability of decreasing investment and a 2 pp lower probability of increasing it than an unconstrained one. Likewise, a constrained firm has a 2.5 pp higher probability of decreasing working capital and a 3 pp lower probability of increasing it than an unconstrained one. Similar figures are found for employment growth (Table 20). Thus, "perceived financing constraints", as measured through firms' self-assessment of the barriers for access to finance, also help explain firm's investment, liquid assets and growth.

Instrumental variables
So far our identification strategy has relied on the use of an extensive set of country-industrywave effects and firm-level covariates to control for firms' investment and growth opportunities. However, if we are not perfectly controlling for investment opportunities, then the error term will be correlated with our credit constraint dummies. Hence, in robustness, we use an instrumental variable to isolate the exogenous part of the key regressors. 45 The proposed instrument, credit standards, is an ordinal variable that measures the level of credit standards of each country in each round of the SAFE. The variable comes from the ECB's Bank Lending Survey (BLS), a quarterly survey that asks euro area banks about developments in their respective credit markets since 2003. 46 44 This question is asked to all firms, unlike the questions used to construct the baseline measure of credit constraints, which are only asked to those firms that consider each source of financing relevant. This is the reason why the estimation sample is larger. 45 Ferrando and Mulier (2015), in their study of the impact of discouragement on investment and firm growth, also follow an IV strategy. Specifically, they instrument their dummy for discouraged borrowers with a firm-level financial constraints indicator, namely a dummy that equals 1 if the firm considers access to finance as the most pressing problem. However, such an instrument is likely to be invalid if lenders observe a firm's lack of investment opportunities and in turn decide to restrain credit, making access to credit the firm's most pressing problem. To put it differently, they instrument a financial constraints indicator, "discouraged borrowers", with another financial constraints indicator, "access to finance as most pressing problem". 46 Currently the sample comprises more than 140 banks from 19 euro area countries, with a coverage of around 60% of the amount outstanding of loans to the private non-financial sector in the euro area. For information about the survey see Köhler-Ulbrich, Hempell and Scopel (2016): "The euro area bank Crucially, the BLS is especially designed to distinguish between supply and demand conditions in the euro area credit markets. Supply conditions are measured with credit standards, credit terms and conditions and the various factors that may have caused them to change. According to the BLS, "credit standards are the internal guidelines or loan approval criteria of a bank. They are established prior to the actual loan negotiation on the terms and conditions and the actual loan approval/rejection decision… Credit standards specify the required borrower characteristics (e.g. balance sheet conditions, income situation, age, employment status) under which a loan can be obtained…" In other words, they are the prespecified criteria an individual or firm must meet to be eligible for certain loan, i.e., the ex-ante probability of granting a loan, keeping the borrower's creditworthiness fixed. Hence, only supply factors can change them: "… Credit standards may change owing to changes in the bank's cost of funds and balance sheet situation, changes in competition, changes in the bank's risk perception, changes in the bank's risk tolerance or regulatory changes, for instance." Therefore, credit standards have little to do with demand factors such as the firm's investment and growth opportunities and they satisfy the exclusion restriction. In addition, tighter credit standards are likely to increase the probability of credit constraints, making them a relevant instrument, as the first-stage will corroborate.
In particular, the BLS asks banks to describe the current level of their credit standards relative to the range of the bank's credit standards between the second quarter of 2010 and the moment the question is formulated 47 , and the participant bank must select an answer out of eight possible options, ranging from very loose to very tight. We first compute the level of credit standards of each bank by assigning values from 1 to 7 to each possible option, with higher values indicating tighter credit standards. 48 We then calculate the average of credit standards for each country in each wave of the SAFE. Summary statistics of the variable are reasonable, as the mean level of credit standards decreases over time (4.6 in wave 11, 4.1 in wave 15) and is higher in vulnerable countries (4.6) than in less vulnerable countries (4.2) 49 , indicating than credit standards in the euro area are slightly tighter than the average of the period (4.3). Notice that, although the same question is asked for five different loan categories (overall loans to enterprises, loans to SMEs, loans to large firms, loans to households for house purchase, loans for consumer credit and other lending), we only exploit the answers to the SME segment because the five series are highly correlated and including more than one of those instruments would lead to weak IVs, as suggested by first-stage F-tests. The results are also robust to replacing the country mean of the ordinal variable by the percentages of banks that answer each option (percentage of banks that report that credit standards are at the lending survey. Role, development and use in monetary policy preparation". Visit also https://www.ecb.europa.eu/stats/ecb_surveys/bank_lending_survey/html/index.en.html 47 The BLS also uses another reference period, between the first quarter of 2003 and the moment of the survey, to measure the level of credit standards. However, we prefer to use the shorter reference period to minimise measurement error, as it may be more difficult to assess the range of credit standards of such a long period. 48 The question and possible answers (with the corresponding numbers in parenthesis) are the following. "How would you describe the current level of your bank's credit standards relative to the range of your bank's credit standards between 2010Q2 and now? (1) At the loosest level during this period. (2) Considerably looser than the midpoint of the range. tightest level, percentage of banks that report that credit standards are moderately tighter than the average of the period, etc) but we also rule out this option to avoid having weak IVs.
In order to implement the IV strategy we first transform our ordinal dependent variables, investment growth, working capital growth and employment growth, into dummies that equal 1 if investment/working capital/employment has increased and 0 if it has decreased or it has remained unchanged. The reason for carrying out such transformation is to be able to use standard models for binary dependent variables with endogenous regressors such as twostage least squares (linear probability model). 50 Second, we merge our two key regressors, cc_bank and cc_other, into a single variable, cc_all, which equals 1 if the firm is constrained in at least one of the two financing sources (i.e., cc_bank=1 and/or cc_other=1) and 0 if the firm is constrained in none of them (i.e., cc_bank=cc_other=0). The reason for such transformation is that, as Angrist and Pischke (2009) argue, models with multiple endogenous variables are hard to identify and the results can be hard to interpret, so we prefer to estimate a regression model with a single endogenous covariate. Finally, as in the previous section, we only keep in the group of unconstrained firms those that successfully applied for a loan. Table 21 presents the results of linear probability models estimated by Ordinary Least Squares (OLS) and Two-stage Least Squares (TSLS) 51 , in which the dependent variable is investment growth. Time-varying controls are still lagged once, but the endogenous regressor cc_all and the instrument credit standards are now included contemporaneously (at time t). Countryindustry-time dummies have been replaced by country, industry and time dummies to allow identification of the instrument, which only varies at the country-time level. 52 However, to rule out that the instrument is correlated with the economic cycle and in turn with the economy-wide investment opportunities, we include, as an additional regressor, the detrended level of real GDP. 53 We also report the first-stage F-statistic and two endogeneity tests on cc_all, one based on the first-stage residuals, as suggested by Wooldridge (2003) 54 , and another one based on the difference of two Sargan statistics. 55 Column (1), estimated by OLS, shows a negative and strong correlation between cc_all and investment growth. However, to establish a causal relationship we need to make use of our 50 Unfortunately, models for ordinal variables with endogenous regressors are much less developed than binary models with instrumental variables. An estimation algorithm is provided by Miranda and Rabe-Hesketh (2006), which estimates, by maximum likelihood, endogenous switching and sample selection models for binary, ordinal and count variables. However, the algorithm fails to converge when it is used in a specification with a large set of dummies, as it is our case. 51 We prefer to estimate linear probability models via TSLS, rather than an IV Probit, because the endogenous regressor is a binary variable, implying that the joint normality assumption of the IV Probit will be violated. See Wooldridge (2004), pages 472-477. 52 Very similar results are found if we introduce country-sector and sector-time dummies. 53 Constructed with a Hodrick-Prescot filter. Very similar results are found when using annual GDP growth or changes in the general economic outlook (whether it has improved, remained unchanged or deteriorated) as reported by firms in the SAFE. 54 Wooldridge 's (2003) endogeneity test is carried out by including the first-stage residuals in the structural equation and testing their significance via a t-test. If they are significant, we reject the null hypothesis of exogeneity. See Wooldridge (2003), pages 506-507.

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The endogeneity test is defined as the difference of two Sargan-Hansen statistics: one for the equation with the smaller set of instruments, where the suspect regressors are treated as endogenous, and one for the equation with the larger set of instruments, where the suspect regressors are treated as exogenous. Under the null hypothesis the specified endogenous regressors can actually be treated as exogenous. Under conditional homoskedasticity, this endogeneity test statistic is numerically equal to a Hausman test statistic; see Hayashi (2000, pp. 233-34). instrumental variable credit standards. According to the first-stage (column 2), a unit increase in the level of credit standards (i.e., tightening) increases the likelihood of being credit constrained by 10 percentage points. The effect is statistically significant at a 1% level. The instrument does not seem to be weak, as the value of the first-stage F-statistic is 15, well above 10, the reference value suggested by Staiger and Stock (1997). In addition, the reduced form equation (column 3) shows that the instrument has a significant effect on the outcome variable, as a unit tightening in the level of credit standards reduces the probability that investment increases by 7.2 pp. The IV estimates that result from the estimation of the firststage and the reduced form are displayed in column (4). According to those estimates, the presence of credit constraints reduces by 71 pp the probability of increasing investment, and the effect is statistically significant at 5%. The two endogeneity tests reject the null hypothesis of exogeneity of cc_all, suggesting that we need to pay more attention to the IV estimates, as OLS is likely to be inconsistent.
The IV estimate is remarkably larger than the OLS estimate, suggesting that the latter underestimate the casual effect of credit constraints on investment growth. Notice that, following Imbens and Angrist (1994), we can interpret the IV estimate as the Local Average Treatment Effect (LATE). The LATE is the average treatment effect on the subpopulation of compliers, who are the individuals whose treatment status changes when the value of the instrumental variable changes as well. IV methods are uninformative for always-takers (the ones that always receive the treatment, irrespective of the value of the IV) and never-takers (the ones that never receive the treatment) because the instrument is unrelated to their treatment status. 56 In our empirical application, compliers are the firms that become financially constrained (unconstrained) when credits standards are tightened (eased), alwaystakers are the firms that are always financially constrained, regardless of the levels of credit standards, and never-takers are the firms that are never financially constrained. 57 Our estimates suggest a strong causal effect on the subpopulation of complier firms, which is expected to differ from the average causal effect for the entire treated population (the treatment effect on the treated) because of the existence of always-takers.
The impact of credit constraints on working capital growth is displayed in Table 22. The OLS estimates (column 1) are marginally significant (at a 10% level), but the IV estimates (column 4) are not statistically different from zero. Nevertheless, the two endogeneity tests fail to reject the null hypothesis of exogeneity of cc_all (p-values around 0.6). Hence, we should rely more on the OLS estimates, which are expected to be consistent and more efficient than the TSLS estimates under exogeneity.
The impact of credit constraints on firm growth is presented in Table 23. According to the OLS estimates (column 1), a credit constrained firm has a 5.4 pp lower probability of increasing employment than an otherwise identical firm, and the effect is statistically significant at 1%. By contrast, the estimation of the structural equation by TSLS (column 4) shows no statistically significant effect of credit constraints on employment growth. However, as the two endogeneity tests fail to reject the null hypothesis of exogeneity of cc_all (p-values around 0.6), we should rely more on the efficient OLS estimates. Therefore, the results of this section suggest a strong causal impact of overall credit constraints on firm investment, while the effects on firm growth and working capital are weaker and less robust.

Conclusions
In frictionless perfect capital markets, the Modigliani-Miller theorem (1958) implies that a firm's financing decisions are independent from its investment decisions because internal and external funds are perfect substitutes. In practice, however, several factors lead to an imperfect substitutability between internal and external funds 58 , so that financial constraints may have important effects on real variables such as investment, inventories and other working capital and firm growth. The purpose of this research is to test this empirical prediction. We do so with the Survey on the access to finance of enterprises (SAFE), a survey that is especially designed to analyse the problems in the access to external finance faced by European SMEs. In particular, we use a panel of about 5,000 small and medium-sized enterprises (SMEs) from 12 European countries for the period 2014-2016. In line with previous studies on the SAFE, we develop several survey-based indicators of credit constraints, distinguishing between constraints in the access to bank finance (bank loans, bank overdrafts, credit lines) and in the access to other finance (trade credit, leasing, factoring, debt and equity securities, etc).
Our goal is to identify the causal effect of credit constraints on investment, inventories and other working capital and firm growth. The key identification challenge we face is a potential reverse-causality bias, as we expect firms with poor investment/growth opportunities to have a higher probability of being credit constrained. We implement several strategies to overcome this obstacle: proxies for investment opportunities, lagged regressors, random effects and instrumental variables. Our findings suggest that credit constraints, both in bank financing and other financing, have strong negative effects on investment in fixed assets, while the impact on firm growth and working capital is less robust.
In addition, we analyse heterogeneous effects by computing average marginal effects for different types of firms. A remarkable result is that micro firms (less than 10 employees) and relatively young companies (less than 5 years old) are largely unaffected by credit constraints, probably because those firms rely more on internal funds (cashflows and retained earnings) to fund their investment projects, making them less sensitive to access to external funds. This result complements those of Beck et al. (2005), who find that small and medium-sized enterprises (SMEs) face greater financial, legal and corruption obstacles compared to large firms and the constraining impact of obstacles on firm growth is inversely related to firm size. However, Beck et al. (2005) compare SMEs with large firms, while we compare micro, small and medium-sized firms. Hence, the effect of credit constraints on firm investment and growth may be a non-monotonic (concave) function of firm size. Moreover, the negative effect of credit constraints on firm outcomes seems to be mainly driven by the impact on family businesses and on sole traders, while most of the other ownership categories (e.g. listed companies, firms owned by other enterprises) are largely unaffected, which is consistent with asymmetric information problems.

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See Fazzari et al. (1988) and Schiantarelli (1996) for a review of the theoretical research in this area.
Our paper contributes to a new emerging strand of the literature that uses survey data to construct direct measures of financial constraints (Campello et al., 2010, Ferrando andMulier, 2015). First, it extends the work of Ferrando and Mulier (2015) on discouraged borrowers to both "formal" and "informal" credit constraints (discouragement, quantity rationing, price rationing, rejected applications) and assesses the role of all sources of financing, not only bank loans, in shaping business decisions. It also covers a larger number of countries and analyses the recovery period of the European economy (2014-2016), unlike previous studies that have focused in the last recession. This is particularly interesting as the Eurozone prepares itself for monetary policy normalisation, which could influence on firms' investment via the so-called balance sheet channel and the external finance premium paid by credit constrained firms (Bernanke and Gertler, 1995). It also looks at the impact of financial constraints on inventories and other working capital, an aspect that has traditionally been overlooked in the literature. Finally, it attempts to establish a causal link between credit constraints and firm investment, inventories and growth by exploiting the panel nature of the data and by making use of an instrumental variable to isolate the exogenous part of credit constraints.
Finally, notice that our results are conservative measures of the total impact of credit constraints in the real economy, as our analysis ignores the extensive margin, i.e., those businesses that shut down because of a lack of credit and those firms that do not enter the market because they do not obtain financing to undertake their investment projects. Table 1: variables description   country  12 European countries  sector industry, construction, wholesale or retail trade, other services size_employment 1 (micro), 2 (small), 3 (medium) size_turnover 1(<= € 500,000), 2 (€500,000 -€1 million), 3 (€ 1 million -€ 2 million) 4 (€ 2 million -€10 million), 5 (€10 million -€50 million), 6 (> € 50 million) age >=10 years, >=5 and <10 years, >=2 and <5 years, <2 years legal form subsidiary or branch, autonomous entreprise ownership structure public shareholders, family or entrepreneurs, other entreprises, venture capital entreprises, one owner only, other exporter 0,1 cc_all 0,1 cc_bank 0,1 cc_other 0,1 problem_access_finance 1-10 investment growth decreased, remain unchanged, increased working capital growth decreased, remain unchanged, increased employment growth decreased, remain unchanged, increased turnover decreased, remain unchanged, increased profits decreased, remain unchanged, increased labour_costs decreased, remain unchanged, increased other_costs decreased, remain unchanged, increased interest_expenses decreased, remain unchanged, increased debt_to_assets decreased, remain unchanged, increased enterprise_outlook improved, remain unchanged, deteriorated enterpise_capital improved, remain unchanged, deteriorated credit_history improved, remain unchanged, deteriorated     0,0519 Dependent variable: investment growth. Controls are dummies for size (in terms of employment and turnover), age, legal form, ownership structure and exporter. Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1). Estimator: ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1). Estimator: ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1). Estimator: ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 Controls are dummies for size (in terms of employment and turnover), age, legal form, ownership structure and exporter. Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1). Country-industry-time dummies included in all specifications. Estimator: ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 Controls are dummies for size (in terms of employment and turnover), age, legal form, ownership structure and exporter. Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1). Country-industry-time dummies included in all specifications. Estimator: ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 Controls are dummies for size (in terms of employment and turnover), age, legal form, ownership structure and exporter. Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1). Country-industry-time dummies included in all specifications. Estimator: ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once. Estimator: random effects ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country. *** p<0.01, ** p<0.05, * p<0.1 Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once. Estimator: random effects ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country. *** p<0.01, ** p<0.05, * p<0.1 Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once. Estimator: random effects ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country. *** p<0.01, ** p<0.05, * p<0.1 0.1208 Dependent variable: investment growth. Controls are dummies for size (in terms of employment and turnover), age, legal form, ownership structure and exporter. Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1). Estimator: ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1). Estimator: ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1). Estimator: ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1) Estimator: ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1) Estimator: ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1) Estimator: ordered probit. Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 The instrumental variable is an indicator of the level of credit standards in loans to SMEs, as reported in the Bank Lending Survey. GDP is detrended with a Hodrick-Prescott filter. Controls are dummies for size (in terms of employment and turnover), age, legal form, ownership structure and exporter. Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1). Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 F-test (first-stage) is the Kleibergen-Paap Wald rk F statistic. The endogeneity test (residuals) is carried out by including the first-stage residuals in the structural equation and testing their significance via a t-test. If they are significant, we reject the null hypothesis of exogeneity. The endogeneity test (difference in Sargan) is defined as the difference of two Sargan-Hansen statistics: one for the equation with the smaller set of instruments, where the suspect regressors are treated as endogenous, and one for the equation with the larger set of instruments, where the suspect regressors are treated as exogenous. Under the null hypothesis the specified endogenous regressors can actually be treated as exogenous. The instrumental variable is an indicator of the level of credit standards in loans to SMEs, as reported in the Bank Lending Survey. GDP is detrended with a Hodrick-Prescott filter. Controls are dummies for size (in terms of employment and turnover), age, legal form, ownership structure and exporter. Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1). Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 F-test (first-stage) is the Kleibergen-Paap Wald rk F statistic. The endogeneity test (residuals) is carried out by including the first-stage residuals in the structural equation and testing their significance via a t-test. If they are significant, we reject the null hypothesis of exogeneity. The endogeneity test (difference in Sargan) is defined as the difference of two Sargan-Hansen statistics: one for the equation with the smaller set of instruments, where the suspect regressors are treated as endogenous, and one for the equation with the larger set of instruments, where the suspect regressors are treated as exogenous. Under the null hypothesis the specified endogenous regressors can actually be treated as exogenous. The instrumental variable is an indicator of the level of credit standards in loans to SMEs, as reported in the Bank Lending Survey. GDP is real GDP detrended with a Hodrick-Prescott filter. Controls are dummies for size (in terms of employment and turnover), age, legal form, ownership structure and exporter. Other controls are dummies for increase/decrease in turnover, profits, labour costs, other costs, debt-to-assets and interest expenses and dummies for improvement/deterioration of enterprise-specific outlook, enterprise's own capital and enterprise's credit history. All time-varying controls are lagged once (t-1). Cluster-robust standard errors in parentheses. Cluster level: country-wave. *** p<0.01, ** p<0.05, * p<0.1 F-test (first-stage) is the Kleibergen-Paap Wald rk F statistic. The endogeneity test (residuals) is carried out by including the first-stage residuals in the structural equation and testing their significance via a t-test. If they are significant, we reject the null hypothesis of exogeneity. The endogeneity test (difference in Sargan) is defined as the difference of two Sargan-Hansen statistics: one for the equation with the smaller set of instruments, where the suspect regressors are treated as endogenous, and one for the equation with the larger set of instruments, where the suspect regressors are treated as exogenous. Under the null hypothesis the specified endogenous regressors can actually be treated as exogenous.