Jérémie Bertrand

Paul-Olivier Klein
email: paul-olivier.klein@univ-lyon3.fr.

Jean-Loup Soula
email: jlsoula@unistra.fr

Liquidity Creation and Trust Environment *

Keywords: G21, G32, Z1 Banking, Liquidity Creation, Trust, Financial Intermediation

 posit that a high trust environment discourages banks from creating liquidity. To address these conflicting views, the current study measures liquidity creation using Berger and Bouwman's (2009) methodology, then assesses the level of trust in the environment with four proxies and two additional instruments deployed in previous research. The results confirm a positive effect of trust in banks on liquidity creation, especially for small or state-chartered banks and during economic downturns. The results are robust to time effects and potential endogeneity concerns.

Introduction

Liquidity creation is a key function performed by banks [START_REF] Bhattacharya | Contemporary Banking Theory[END_REF], as well as a prominent measure of banking activity [START_REF] Berger | Bank Liquidity Creation and Real Economic Output[END_REF]. Banks create liquidity by funding illiquid assets, such as long-term loans, with liquid liabilities, such as deposits. The central role of liquidity creation in banks' activities and its importance for the economy has been demonstrated theoretically [START_REF] Diamond | Bank Runs, Deposit Insurance, and Liquidity[END_REF][START_REF] Kashyap | Banks as Liquidity Providers: An Explanation for the Coexistence of Lending and Deposit-taking[END_REF]. More recently, [START_REF] Berger | Bank Liquidity Creation[END_REF] offered a measure of bank liquidity creation, that allows for further empirical investigation. A great literature utilizes Berger and Bouwman's measure to assess the relationship between bank liquidity creation and various economic outcomes (e.g. [START_REF] Fidrmuc | Does Bank Liquidity Creation Contribute to Economic Growth? Evidence from Russia[END_REF][START_REF] Berger | Bank Liquidity Creation and Real Economic Output[END_REF][START_REF] Casu | Liquidity Creation and Bank Capital[END_REF].

While there exist different factors that affect liquidity creation, such as competition [START_REF] Horvath | How Bank Competition Influences Liquidity Creation[END_REF][START_REF] Jiang | Competition and Bank Liquidity Creation[END_REF], capital regulation (Caso, Pietro and Ponce, 2018;[START_REF] Berger | Bank Liquidity Creation Following Regulatory Interventions and Capital Support[END_REF] or deposit insurance (Fungacova, Weill and Zhou, 2017), seminal banking literature posits liquidity creation to be intrinsically dependant of one key factor: trust environment [START_REF] Diamond | Bank Runs, Deposit Insurance, and Liquidity[END_REF][START_REF] Diamond | Liquidity Risk, Liquidity Creation, and Financial Fragility: A Theory of Banking[END_REF].

Trust plays an essential role in economic transactions. Among others, [START_REF] Algan | Inherited Trust and Growth[END_REF] show the role of inherited trust on economic growth. Measuring trust as social capital, [START_REF] Guiso | The Role of Social Capital in Financial Development[END_REF] document how it encourages financial development, and [START_REF] Lins | Social Capital, Trust, and Firm Performance: The Value of Corporate Social Responsibility during the Financial Crisis[END_REF] reveal its effect on corporations. [START_REF] Guiso | Trusting the Stock Market[END_REF] demonstrate the role of people general trust on stock market developments. In credit markets, trust is also an essential component. A vast literature shows the positive role trust exerts on credit market development [START_REF] Becchetti | Enhancing Capabilities Through Credit Access: Creditworthiness as a Signal of Trustworthiness Under Asymmetric Information[END_REF] -trust is measured through an experiment), access to credit [START_REF] Moro | Loan Managers' Trust and Credit Access for SMEs[END_REF]Tang, Deng, and Moro, 2017 -trust is measured at the loan officer level) and lending terms [START_REF] Kim | Impact of Ethical Behavior on Syndicated Loan Rates[END_REF] -trust is measured as repeated non-opportunistic behaviours). Such studies imply two perspectives on trust: specific trust between the client and the bank [START_REF] Tang | Firm-Bank Trusting Relationship and Discouraged Borrowers[END_REF] and an environment of trust in which a bank operates [START_REF] Guiso | The Role of Social Capital in Financial Development[END_REF].

We focus on this latter form, the environment of trust, in an attempt to document its impact on banking activity, measured as liquidity creation. Such an approach is similar to the one adopted by [START_REF] Algan | Inherited Trust and Growth[END_REF], [START_REF] Guiso | The Role of Social Capital in Financial Development[END_REF] [START_REF] Guiso | Trusting the Stock Market[END_REF] and follows a strand in the literature that emphasizes the importance of the degree of trust in the economy (e.g., [START_REF] Grayson | Is Firm Trust Essential in a Trusted Environment? How Trust in the Business Context Influences Customers[END_REF], and notably the role of trust as a social norm for economic transactions [START_REF] Basov | Optimal Contracting Model in a Social Environment and Trust-Related Psychological Costs[END_REF]. Evidence suggests that the trust environment plays a critical role in the provision of loans (e.g. [START_REF] Chen | Social Trust and Bank Loan Financing: Evidence from China[END_REF] and the supply of bank services (e.g. [START_REF] Järvinen | Consumer Trust in Banking Relationships in Europe[END_REF] We extend this perspective and focus on the impact of the environment of trust on banks' liquidity creation.

Employing this perspective, we also attempt to resolve a debate among theoretical approaches to banking. Theoretical literature identifies the environment of trust in which banks evolve as a crucial factor affecting liquidity creation but posits two opposite predictions. On the one hand, in a trust environment, banks might collect deposits more easily, which would reduce the costs associated with a liquidity mismatch, as when they are forced to liquidate assets to meet depositors' demands [START_REF] Diamond | Bank Runs, Deposit Insurance, and Liquidity[END_REF][START_REF] Allen | The Theory of Financial Intermediation[END_REF][START_REF] Allen | Financial Intermediaries and Markets[END_REF]. Higher trust in banks also reduces the risk of switching depositors and the risks of runs for all banks [START_REF] Saparito | The Role of Relational Trust in Bank-Small Firm Relationships[END_REF], which facilitate the transformation of maturities. This view predicts a positive link between trust in banks and liquidity creation. But on the other hand, an environment of trust might limits banks' incentives to create liquidity. Diamond andRajan (2000, 2001) propose that financial fragility prompts liquidity creation; due to the discipline that depositors exert on banks, banks have incentives to monitor and grant loans to risky borrowers. According to [START_REF] Myers | The Paradox of Liquidity[END_REF], liquidity creation also can be used as a signal by banks to demonstrate that they are acting in the interest of their depositors and lending for the long-run, which prevents them from substituting assets [START_REF] Flannery | Debt Maturity and the Deadweight Cost of Leverage: Optimally Financing Banking Firms[END_REF].

From this perspective, greater general trust in banks reduces their incentives to elicit more trust, by creating liquidity, implying a negative link.

With this study, we seek to identify which prediction dominates and if a positive environment of trust is conducive to or obstructs liquidity creation, as an overall indicator of banking activity.

We employ quarterly U.S. bank data, obtained from the Federal Reserve Call Reports, and run panel fixed effect regressions with banks and macroeconomic controls for the period from 1985 to 2016. We use [START_REF] Berger | Bank Liquidity Creation[END_REF] bank liquidity creation measure. Then to estimate the level of trust in the economy, we employ the annual value of "Trust in Banks" reported by the Gallup survey. This type of survey measure has already been widely employed in the literature (e.g. [START_REF] Knell | Trust in Banks during Normal and Crisis Times-Evidence from Survey Data[END_REF][START_REF] Jansen | When Does the General Public Lose Trust in Banks[END_REF][START_REF] Stevenson | Trust in Public Institutions over the Business Cycle[END_REF]. It provides a direct measure of respondents' trust in financial institutions, without relying on proxies. Because we use quarterly fixed effects in the regression, we can rule out time effects while also capturing time trends in our sample.

Then we integrate three alternative indicators of the trust environment to address potential issues with the survey-based variable. First, we use the occurrence of a financial crisis as an exogenous source of a drop in trust, similar to [START_REF] Sapienza | A Trust Crisis[END_REF]. We refine the measure by distinguishing financial versus banking crises too [START_REF] Berger | How Does Capital Affect Bank Performance During Financial Crises[END_REF].

Second, we determine the number of past bank failures by state, as a source of state-specific decreases in trust [START_REF] Jansen | When Does the General Public Lose Trust in Banks[END_REF][START_REF] Van Der Cruijsen | Trust and Financial Crisis Experiences[END_REF]. Third, similar to Guiso (2010), we consider victims of Madoff's Ponzi scam as an exogenous source of diminished trust. With state-level variables, we benefit from more granularity; shocks to trust offer us means to tackle potential endogeneity issues. We further address endogeneity concerns with instrumental variables regressions, using two different instruments.

We first estimate the effect of the environment of trust in banks on the overall liquidity creation and then distinguish between on-and off-balance-sheet liquidity creation. To establish additional findings, we explore situations in which a strong environment of trust likely has a more critical role. In particular, and in line with prior liquidity creation literature (e.g., Berger andBouwman 2009, 2013), we explore differences in the relation between trust and liquidity creation according to bank sizes. Small and large banks likely react differently to modifications in the trust environment, because they do not have access to the same pool of depositors and rely on different lending technologies. Big banks (too big to fail) benefit from an implicit state guarantee that also should affect the relationship between trust and liquidity creation. Moreover, we explore banks' charters, which reflect different levels of proximity between banks and their customers and thus could alter the relationship between the trust environment and liquidity creation. Both trust [START_REF] Stevenson | Trust in Public Institutions over the Business Cycle[END_REF] and liquidity creation [START_REF] Berger | Bank Liquidity Creation, Monetary Policy, and Financial Crises[END_REF] also are affected by business cycles; trust is especially critical during economic downturns, and liquidity creation is often hampered by recessions [START_REF] Berger | Bank Liquidity Creation and Financial Crises[END_REF]. To document these effects, we track the evolution of the relationship between liquidity creation and trust over the business cycle.

The main results reveal a positive relationship between trust in banks and liquidity creation, spanning overall, on-, and off-balance sheet forms of liquidity creation. We confirm this positive relationship across multiple specifications, alternative trust variables, and instrumented regressions. The link is particularly positive and significant when the trust environment is more critical, namely, for small banks, state-chartered banks, and during economic downturns. With these findings, this study contributes to two main strands of literature. First, we address a theoretical debate about the relationship between general trust and liquidity creation and thereby provide a conclusive resolution to the contradictory predictions by [START_REF] Diamond | Bank Runs, Deposit Insurance, and Liquidity[END_REF] and [START_REF] Diamond | Liquidity Risk, Liquidity Creation, and Financial Fragility: A Theory of Banking[END_REF] that underlie banking intermediation theories. Second, we expand insights into liquidity creation by showing that norms and values, including trust, exert strong influences on liquidity created by banks, which constitutes their main output [START_REF] Berger | Bank Liquidity Creation and Real Economic Output[END_REF].

To establish these contributions, in Section 2 we review prior literature as a foundation for developing the hypotheses. Section 3 contains the data and methodology. In Section 4, we discuss the results, followed by robustness analyses in Section 5 and conclusions in Section 6.

Literature and Hypotheses

We focus our discussion on the two opposing theoretical predictions for how an environment of trust in banks might affect banks' liquidity creation, to frame our hypotheses. [START_REF] Diamond | Bank Runs, Deposit Insurance, and Liquidity[END_REF] predict a positive impact of a trust environment on banks' activities and liquidity creation. Their perspective focuses on depositors. A more trusting public makes the collection of deposits easier and reduces the risk of sudden withdrawals or bank runs.

The Positive Effect of Trust on Liquidity Creation

That is, more trusting depositors are less likely to withdraw their funds at short notice [START_REF] Saparito | The Role of Relational Trust in Bank-Small Firm Relationships[END_REF]. With the resulting stable source of funding, banks can create liquidity on the asset side, which reduces banks' liquidity mismatch concerns and fosters their financial soundness. As Allen and Santomero (1998) and [START_REF] Allen | Financial Intermediaries and Markets[END_REF] document, liquidity mismatches are costly for banks. If they face an unexpected shortage of deposits for example, banks might be forced to conduct a fire sale of their assets, which has substantial costs. By lowering the risk of unexpected withdrawals, trust reduces the likelihood of liquidity mismatches and thus their costs, which also might encourage banks to engage in more asset transformation. Thus, trust should enhance liquidity creation.

Trust among depositors also increases banks' resilience to financial shocks. Runs are triggered by a sudden, common fear that the bank cannot refund depositors [START_REF] Chari | Banking Panics, Information, and Rational Expectations Equilibrium[END_REF], usually rooted in some severe harm to the bank's financial soundness that appears to threaten its capacity [START_REF] Shin | Reflections on Northern Rock: The Bank Run That Heralded the Global Financial Crisis[END_REF]. However, [START_REF] Chari | Banking Panics, Information, and Rational Expectations Equilibrium[END_REF] show that a bank run can start even without any adverse information. According to [START_REF] Diamond | Bank Runs, Deposit Insurance, and Liquidity[END_REF], a depositor run is a potential outcome of deposit contracts, even without any exogenous event [START_REF] Postlewaite | Bank Runs as an Equilibrium Phenomenon[END_REF]. The trigger is some damaged perception of the bank's ability to refund deposits, which sparks a shared fear. Thus, even without any objective drop in banks' financial stability, runs become self-realizing. The famous example of A.P. Giannini halting a run on Bank of America in 1906, by physically piling up gold reserves on the street, also shows how restoring trust (i.e., by demonstrating the institution's soundness) can reduce panic and avoid bankruptcy [START_REF] Berger | The Oxford Handbook of Banking[END_REF]. By stabilizing deposits, trust by the public in banks increases their risk-bearing capacity [START_REF] Repullo | Capital Requirements, Market power, and Risk-Taking in Banking[END_REF], so banks can lend more and create more liquidity.

Overall, this first view posits that a positive trust environment ensures a smooth stream of deposits, which mitigates the costs associated with liquidity mismatch and fosters banks' financial soundness. This enables banks to further transform maturities and to take additional risks, generating more loans and exerting a positive effect on liquidity creation.

The Negative Effect of Trust on Liquidity Creation

If banks create liquidity to gain public trust, trust instead might have a negative effect on liquidity, as depicted by two models, reflecting the asset and the liability sides.

First, [START_REF] Diamond | Liquidity Risk, Liquidity Creation, and Financial Fragility: A Theory of Banking[END_REF] take the perspective of entrepreneurs to argue that financial fragility explains why banks fund long-term loans with short-term deposits (i.e., create liquidity). That is, lenders face credibility issues when lending to entrepreneurs: they are unlikely to conduct a fire sale of assets as soon as liquidity needs arise, because doing so would impose high costs. The bank needs a credible reason to conduct a fire sale, such that not liquidating the project outweighs the liquidation costs. The solution is a very liquid type of funding, such as deposits. Runs threaten the existence of the lender and provide a justification for a fire sale of assets, in case of liquidity needs. Financial instability due to short-term deposits and long-term loans gives credibility to the bank. From this perspective, financial fragility is actually the root of liquidity creation and excessive trust would have an adverse effect. If depositors unconditionally trust banks, there is little threat of a run, which reduces the credibility of a bank and its bargaining power relative to entrepreneurs. Ultimately, it might reduce the incentives of the bank to fund long-term loans and create liquidity. 5Second, accrued public trust may reduce banks' need to demonstrate their trustworthiness to depositors. For [START_REF] Myers | The Paradox of Liquidity[END_REF], liquidity creation is the raison d'être of banks; it allows them to demonstrate that they act in the best interest of clients. Depositors may be concerned that banks misuse deposits though, and bankers may face moral hazard concerns when they use deposits to fund investment projects. Thus they might substitute assets, take excessive risk, or divert deposits for their own sake [START_REF] Flannery | Debt Maturity and the Deadweight Cost of Leverage: Optimally Financing Banking Firms[END_REF]. Funding illiquid longterm loans with short-term liquid deposits is an effective way for bankers to send credible signals to depositors: They put themselves at risk and demonstrate their goodwill. From this perspective, liquidity creation is the best solution to mitigate bankers' moral hazard, but accrued public trust in banks would seize up this mechanism. If the public trusts banks, without making that trust conditional on their behavior, bankers lack incentives to demonstrate their goodwill by lending in the long run, so they may engage in less liquidity creation.

In turn, this second view suggests that banks engage into liquidity creation to gain public trust, in the form of credibility on the asset side [START_REF] Diamond | Liquidity Risk, Liquidity Creation, and Financial Fragility: A Theory of Banking[END_REF] or on the liability side [START_REF] Myers | The Paradox of Liquidity[END_REF]. General trust in banks reduces the strength of these mechanisms, as well as the incentives of banks to secure public trust by transforming assets and maturities.

From this perspective, a trust environment might reduce liquidity creation.

Testable Hypotheses

Our literature review yields two main testable hypotheses. First, according to Diamond and Dybvyg (1983), a better trust environment should reduce mismatch risks and allow banks to engage in maturity transformation, so we hypothesize: H1: an increase in trust environment is associated with an increase in liquidity creation.

Second, turning to [START_REF] Diamond | Liquidity Risk, Liquidity Creation, and Financial Fragility: A Theory of Banking[END_REF], we instead might predict that an environment with more trust in banks reduces the incentives of banks to elicit and sustain that trust, by creating more liquidity. Then we predict: H2: An increase in trust environment is associated with a decrease in liquidity creation.

Data and Methodology

We adopt an empirical approach to determine which hypothesis is valid and to resolve existing, conflicting perspectives. In this section, we present the data and methodology used to conduct the analysis.

Data

Using bank data from quarterly Call Reports on all U.S. banks, spanning 1986Q1-2016Q4 and cleaned as [START_REF] Berger | Bank Liquidity Creation[END_REF], we obtain 38,218 observations of 3,555 U.S. banks.

To estimate their liquidity creation, we gather data from Christa Bouwman's website6 and apply [START_REF] Berger | Bank Liquidity Creation[END_REF] "cat fat" measure (LC), which includes both on-and off-balance sheet items. It classifies each item by assigning a weight according to the liquidity created.

Liquid liabilities and illiquid assets take weights of 0.5, illiquid liabilities and liquid assets are weighted -0.5, and some items have 0 weight. We then multiply the value of each item with its weight and sum them, which produces a value, in USD, of the liquidity created by the bank. To clarify the effect of trust on different components of liquidity creation, we separate this "cat fat" measure into on-and off-balance sheet components (respectively, LC_BS and LC_OFF).

Following prior literature (Berger andBouwman 2009, 2013), we normalize all liquidity creation measures by banks' gross total assets (GTA).

Studies that investigate the impact of the trust environment (e.g., [START_REF] Algan | Inherited Trust and Growth[END_REF]Guiso, Sapienza, andZingales, 2004, 2008) establish that the key consideration is trust as a social norm, not trust at the individual level. Trust environment and individual trust are radically different and are measured differently. In this paper we focus on the trustworthy environment in which banks operate, and refrain from analyzing the role of specific bank-client relationships, as documented by [START_REF] Tang | Firm-Bank Trusting Relationship and Discouraged Borrowers[END_REF], among others. Our measure Trust in Banks relies on the Gallup surveys, collected annually, at the beginning of each calendar year. The survey asks, "Please tell me how much confidence you, yourself, have in banks-a great deal, quite a lot, some, or very little?" People also can indicate a "no opinion" answer. The use of survey data to account for trust in banks provides a direct estimate, not based on proxies. Thus, to establish average public confidence in banks, we created a confidence average of the distribution of answers in Table 1, by assigning a score to each modality (none = 0, very little = 1, some = 2, quite a lot = 3, and great deal = 47 ). The weighted average of the answers is in the last column in Table 1 and equals to about 2, or some confidence. However, in such surveys, people who do not know how to answer tend to give an average response, which may be why we observe a peak for the "some" modality. To avoid this bias, we focus on "great deal" responses. 8 Similar approaches have been adopted by various authors, such as [START_REF] Knell | Trust in Banks during Normal and Crisis Times-Evidence from Survey Data[END_REF], [START_REF] Jansen | When Does the General Public Lose Trust in Banks[END_REF], and [START_REF] Stevenson | Trust in Public Institutions over the Business Cycle[END_REF]. That is, liquidity creation follows stable trends, but trust varies more year-to-year.

Methodology

To examine the relationship between liquidity creation and the trust environment, we employ the following panel fixed effects ordinary least squares regression:

𝐿𝐼𝑄𝐶𝑅𝐸𝐴𝑇 𝑖,𝑡 = 𝛼 + 𝛽 * 𝑇𝑟𝑢𝑠𝑡 𝑖𝑛 𝑏𝑎𝑛𝑘𝑠 𝑡 + 𝜸 + 𝝑 * 𝑾 𝑖,𝑡 + 𝑣 𝑖 + µ 𝑡 + 𝜀 𝑖,𝑡 , (1)
where i denotes the bank, t indicates the quarter, 𝐿𝐼𝑄𝐶𝑅𝐸𝐴𝑇 𝑖,𝑡 is the liquidity creation measure (𝐿𝐶/𝐺𝑇𝐴 𝑖,𝑡 , 𝐿𝐶_𝐵𝑆/𝐺𝑇𝐴 𝑖,𝑡 , 𝑜𝑟 𝐿𝐶_𝑂𝐹𝐹/𝐺𝑇𝐴 𝑖,𝑡 ), and 𝑇𝑟𝑢𝑠𝑡 𝑖𝑛 𝐵𝑎𝑛𝑘𝑠 𝑡 is our measure of trust.

Because the Gallup surveys are conducted at the beginning of each year, we use current values of Trust in Banks. Furthermore, 𝒁 𝑖,𝑡-1 is a vector of bank controls, linked to the associated vector of coefficients 𝜸. We use the lag of these variables to avoid endogeneity concerns.

Specifically, we account for the bank's size, using the natural logarithm of gross total assets (log(GTA)). We control for different levels of capitalization with banks' equity ratio, defined as equity over GTA. We also control for the bank's default risk using the z-score [START_REF] Laeven | Bank Governance, Regulation and Risk Taking[END_REF], defined as the return on assets (ROA) plus a ratio of equity capital to GTA, divided by the standard deviation of ROA. A higher z-score implies a lower default probability.

For these control variables, we obtain data from the Call Reports. In addition to bank-specific variables, we control for general economic conditions, so 𝑾 𝒊,𝒕 represents a vector of macroeconomic variables, linked to an associated vector of coefficients 𝝑. For the annual gross domestic product (GDP) growth, market capitalization, inflation rate, and unemployment rate, we gather information from the World Bank. To control for competition at the state level, we use the Herfindahl-Hirschman index as a measure of loan market concentration. Finally, 𝑣 𝑖 and µ 𝑡 are bank and time fixed effects, respectively and if included, and 𝜀 𝑖,𝑡 is the random error.

The Appendix contains definitions of all the variables and their sources.

Results

In presenting our results, we first focus on the main specification for the full sample, then move to the subsamples.

Main Results

Regarding the relationship between trust and liquidity creation, the first two columns in Table 4 provide The impact of the control variables on liquidity creation also confirms previous results. That is, bank size is positively associated with liquidity creation [START_REF] Berger | Bank Liquidity Creation[END_REF]; the effect of the capital ratio is negative [START_REF] Casu | Liquidity Creation and Bank Capital[END_REF]; risk negatively affects liquidity creation [START_REF] Berger | Bank Liquidity Creation[END_REF]; and higher competition negatively affects liquidity creation [START_REF] Horvath | How Bank Competition Influences Liquidity Creation[END_REF].

However, public trust may affect on-and off-balance sheet liquidity creation differently. For example, Jansen, Mosch, and van der Cruijsen (2015) identify a negative relationship between public trust and the use of opaque products, such as special purpose vehicles or derivatives.

Therefore, we refine our results by distinguishing these effects. On-balance sheet liquidity creation mainly refers to deposits and long-term loans; off-balance sheet liquidity creation stems from banks' guarantees, commitments, derivatives, and participation [START_REF] Berger | Bank Liquidity Creation[END_REF]. This second form of liquidity creation is not trivial; as [START_REF] Berger | Bank Liquidity Creation and Real Economic Output[END_REF] point out that off-balance sheet forms account for 50% of U.S. banks' liquidity creation.

In columns 3-6 of Table 4, we report these differentiated estimations. Trust exerts a consistently positive impact on liquidity creation, even when we control for quarter and bank fixed effects.

The coefficient of Trust in Banks is positive and significant in each case, but the magnitude differs notably. Depending on the specification, the effect of trust on liquidity creation is 3 to 4 times stronger for on-balance sheet items. This finding gives some credence to the idea that trust in banks primarily fosters depositors' trust and allows banks to grant more illiquid loans.

Subsample Analysis

We now document the relationship between trust and liquidity creation from three perspectives: size, charter, and business cycles. Each approach has potential consequences for this relationship. Therefore, we introduce specific predictions for each case, followed by the relevant results. We want to determine if the trust environment is more acute in specific situations that tend to feature a greater role of trust.

Bank Size

Size has a crucial role in liquidity creation (Berger andBouwman, 2009, 2013;[START_REF] Jiang | Competition and Bank Liquidity Creation[END_REF], and big banks are the main creators of liquidity. According to [START_REF] Berger | Bank Liquidity Creation[END_REF], they represent 2% of U.S. banks in number but create 81% of liquidity. They also generate more off-balance sheet liquidity. In addition, big banks tend to use more transactional approaches in lending [START_REF] Berger | Small Business Credit Availability and Relationship Lending: The Importance of Bank Organisational Structure[END_REF], with a reduced role of monitoring, whereas small banks often focus on relationship lending, which may make them more sensitive to environmental trust effects [START_REF] Saparito | The Role of Relational Trust in Bank-Small Firm Relationships[END_REF]. Moreover, banks have access to different types of deposits, depending on their size. Large banks tend to rely on national and international depositors; smaller banks depend more on local ones. Trust is essentially an interpersonal element, so small banks may be more sensitive to changes in the levels of depositors' trust. Many large banks are also denoted "Systemically Important Financial Institutions," so their deposits are implicitly guaranteed from the State, and they face less risk of bankruptcy (O'Hara and Shaw, 1990), which likely tempers the effect of depositors' trust on their liquidity creation. Finally, large banks tend to rely more on derivatives to create liquidity.

To test the resulting effects of size on the trust-liquidity creation relationship, we adopt Berger and Bouwman's ( 2009) thresholds to create size classes: Large banks possess GTA above US$3 billion, medium banks have GTA between $1 billion and $3 billion, and small banks possess GTA less than $1 billion. In Table 5, we report the relationships of trust with liquidity creation overall, on-balance sheet, and off-balance sheet. It reveals that the positive relationship between trust and liquidity creation overall is only valid for small banks but nonsignificant for medium and large banks. Similarly, the positive relationships of trust with on-and off-balance sheet creation both are valid only for small banks. For medium banks, the relationship of trust with off-balance sheet liquidity creation even is negative, in line with the view that more trust leads (medium-sized) banks to reduce their use of off-balance sheet items. This finding appears linked specifically to the use of derivatives by medium banks, whereas small banks reinforce their commitment to and engagement with local firms. Overall then, we confirm that trust primarily matters for small banks, which mainly source their deposits locally, such that they are more affected by changes in trust. They also lack too-big-to-fail status, which reinforces the importance of trust.

Banks' Charters

In the U.S. banking system, banks can be chartered at the state (state chartered) or federal (national chartered) level. Several historical reasons explain this dual-banking system [START_REF] White | To Establish a More Effective Supervision of Banking": How the Birth of the Fed Altered Bank Supervision[END_REF]. Deregulation since the 1980s has eroded the distinction between state and national charters somewhat [START_REF] Blair | Challenges to the Dual Banking System: The Funding of Bank Supervision[END_REF], allowing for fiercer competition across states [START_REF] Stiroh | Competitive Dynamics of Deregulation: Evidence from U.S. Banking[END_REF], but substantial differences between the two types of banks persist and affect their abilities to conduct activities in different states [START_REF] Johnson | Assessing a Decade of Interstate Bank Branching[END_REF].

Therefore, we estimate the impact of a bank's state versus national charter on the relationship between trust and liquidity creation, with the prediction that trust relates to proximity. State-chartered banks have access to local pools of depositors, so they may depend more on trust to create liquidity. They also may tend to lend mostly to state-level borrowers, which would reinforce this role of trust in lending. National-chartered banks instead can diversify their sources of funding more easily, so they depend less on depositors' trust to create liquidity.

When we estimate the model for each subsample, as detailed in Table 6, we find that the relationship between liquidity and trust is significant and positive only for state-chartered banks; for national-chartered banks, the relationship is positive but nonsignificant. Thus proximity appears to influence the relationship between trust and liquidity creation. For banks that source their deposits locally, trust fosters liquidity creation, a result that aligns with the findings regarding bank size and emphasizes the interpersonal role of trust in contributing to liquidity creation. For on-balance sheet liquidity creation, we find similar results, though the results for off-balance sheet liquidity creation are more mixed, indicating similarly negative effects for both types of charters.

Business Cycle

Trust plays a crucial role during economic downturns for firms [START_REF] Lins | Social Capital, Trust, and Firm Performance: The Value of Corporate Social Responsibility during the Financial Crisis[END_REF]. To overcome some of the negative effects of recessions, firms might take advantage of the trust relationships they already have established, such as by expanding their use of trade credit [START_REF] Wu | Trust and the Provision of Trade Credit[END_REF]. Similar mechanisms operate in the banking industry. Banks build relationships with clients and depositors that allow them to maintain their activities even during bad times, in both interbank markets [START_REF] Bräuning | Relationship Lending in the Interbank Market and the Price of Liquidity[END_REF] and retail banking [START_REF] Bolton | Relationship and Transaction Lending in a Crisis[END_REF]. Accordingly, we expect trust to be more important for liquidity creation during economic downturns. Greater trust encourages a flight-to-quality [START_REF] Gatev | Banks' Advantage in Hedging Liquidity Risk: Theory and Evidence from the Commercial Paper Market[END_REF], so trusted banks might collect and retain deposits more easily, such that they can expand their lending. This effect might be especially relevant for banks that primarily use relationship lending [START_REF] Beatriz | Relationship Lending and SMEs' Funding Costs Over the Cycle: Why Diversification of Borrowing Matters[END_REF]. To test the effect of the business cycle, we split our sample according to the output gap, a common measure of business cycles that also relates to inflation expectations and monetary policies [START_REF] Orphanides | The Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time[END_REF]. With data from the Bank of International Settlements, we calculate the output gap as the difference between realized and expected GDP (i.e., negative output gap means growth is below expectations).

Table 7 contains the results for both negative (columns 1-3) and positive (columns 4-6) output gaps; the positive impact of trust on liquidity creation holds only when the output gap is negative, that is, during recessions. The relationship becomes nonsignificant when the output gap is positive. Thus, during downturns, trust allows banks to create more liquidity by facilitating their deposit collection and long-term lending, and these findings hold for both onand off-balance sheet liquidity creation. [START_REF] Thakor | Do Loan Commitments Cause Overlending?[END_REF] similarly shows with a model that banks create more off-balance sheet liquidity during economic booms, to avoid a failure to honor previous engagements that might damage their reputation. They do so even if interest rates are higher, and they may be interested in changing their lending terms. If banks benefit from higher trust in general though, they may be less concerned about damaging their reputation, which results in less off-balance sheet liquidity creation.

Robustness Analyses

We perform two robustness tests to confirm the validity of our results. First, we check three alternative measures of trust in banks to address the potential issues associated with our main measure. Second, we employ two instruments to deal with endogeneity concerns associated with our main measure of trust.

Alternative Measures of Trust

Although the Gallup survey provides a convenient, widely used estimate of trust (e.g., [START_REF] Stevenson | Trust in Public Institutions over the Business Cycle[END_REF], it creates potential concerns about endogeneity and granularity.

Therefore, we consider three alternative measures.

Experience of a Financial Crisis

Financial crises undermine confidence in financial and banking systems [START_REF] Knell | Trust in Banks during Normal and Crisis Times-Evidence from Survey Data[END_REF], as well as trust at the individual level (van der Cruijsen, de Haan, and Jansen, 2016), and these exogenous shocks have long-lasting effects [START_REF] Lins | Social Capital, Trust, and Firm Performance: The Value of Corporate Social Responsibility during the Financial Crisis[END_REF]. With the logic that financial crises constitute shocks to trust levels, we also acknowledge that the shock might be positive or negative, depending on the type of crisis. [START_REF] Berger | How Does Capital Affect Bank Performance During Financial Crises[END_REF] distinguish two types of crises that affect banks differently: banking and market. Banking crises originate from a failure in the banking system and hamper trust in banks. Market crises instead originate outside of the banking system and may foster trust in banking institutions, relative to the market, and generate flights-to-quality by depositors to banks [START_REF] Gatev | Banks' Advantage in Hedging Liquidity Risk: Theory and Evidence from the Commercial Paper Market[END_REF].

In Table 8, we present these two measures of trust, in accordance with Berger and Bouwman's (2013) classification of crises and crisis periods. The results confirm our predictions: A banking crisis is associated with reduced liquidity creation, likely signaling a drop in trust of banks. A market crisis instead is associated with an increase in liquidity creation, in line with reinforced trust in banking institutions. These findings also are consistent for liquidity creation overall, as well as on-and off-balance sheet liquidity creation.

Experience of Bank Failures

As [START_REF] Knell | Trust in Banks during Normal and Crisis Times-Evidence from Survey Data[END_REF] show, fewer banking failures are associated with more trust in banks. [START_REF] Jansen | When Does the General Public Lose Trust in Banks[END_REF] and van der Cruijsen, de Haan, and Jansen (2016) even use previous bank failures to measure a loss of trust in financial institutions. With the same logic, we include the number of bank failures per state and year as alternative measures of trust, which offer two advantages relative to our main measure. First, the information is available at the state level, which increases the granularity of our estimations. Second, even if greater liquidity creation affects the probability of bank failure [START_REF] Fungáčová | High Liquidity Creation and Bank Failures[END_REF], the number of bank failures remains relatively low over the entire period, tempering endogeneity concerns. Because we use a measure at the state level, we only include banks present in one state, removing those that operate in multiple states, so we can properly identify the link between trust and liquidity creation for each state.

The results in Table 8, using the bank failure rate (number of bank failures divided by the number of banks, obtained from the FDIC) as a proxy for trust in banking institutions, affirms our predictions. By using the failure rate from the previous year, we isolate the effect on liquidity creation by banks that did not fail and rule out any mechanical effect. An increase in this lagged bank failure rate is associated with a decrease in liquidity creation, which we posit is an effect of a decrease in trust in banks. This results hold for liquidity creation overall and on-balance sheet liquidity creation, but the coefficient is not significant for off-balance sheet liquidity creation. This result may arise because most large banks are not represented in the sample, which are the ones that primarily use off-balance sheet products. 9

Madoff Scam

Finally, we include the number of victims of the Madoff scam 10 as an alternative measure of a drop in trust [START_REF] Guiso | Implications for the Future of Financial Markets[END_REF][START_REF] Bertsch | The Role of Trust in Online Lending[END_REF]. This alternative approach provides two main 9 Including the whole sample in the estimation yields a negative, significant coefficient for Bank Failure for both on-and off-balance sheet liquidity creation. 10 Bernard Madoff's fraud resulted in a momentous scandal in 2008. He ran a multi-billion-dollar Ponzi scheme for almost 20 years and defrauded more than 4,000 people, both individuals and institutions, for which he was sentenced to 150 years in prison. [START_REF] Hurt | Evil has a new Name (and a new Narrative): Bernard Madoff[END_REF] provides a detailed narrative of the scam.

advantages. First, it is available at the state level, so again we gain granularity in our estimates.

For consistency, we restrict the sample to banks operating in one state only. Second, it represents an exogenous shock in trust that is not affected by liquidity creation, so we can rule out reverse causality. The results in Table 8 again are consistent with our prior findings:

Liquidity creation overall decreases with the number of victims of the scam. Thus, diminished public trust in financial institutions diminishes banks' liquidity creation.

Instrumental Variables

To ensure that variations in trusts in the environment are exogenous, we use instrumental variables to reflect Trust in Banks that help isolate the effect of exogenous variations in trust.

That is, in line with [START_REF] Guiso | The Role of Social Capital in Financial Development[END_REF], we employ electoral turnout as a first instrument. It reflects other aspects of trust in society, such as public engagement and trust in institutions [START_REF] Putnam | Making democracy work: Civic traditions in modern Italy[END_REF]. Compared with survey answers, electoral turnout also provides a more objective measure of public engagement. However, it may reflect elements that are not directly linked to trust, such as moral values or group appurtenances. For our purposes, we regard electoral turnout as a potentially reliable instrument for Trust in Banks, because it is unlikely to affect liquidity creation directly but is likely to be linked at least partially with trust in society, including banks. The U.S. Electoral Project provides electoral turnout data every two years, at the state level.

To compensate for the lack of annual data, we employ a second instrument that may capture the exogenous aspect of trust in banks: trust in Congress, which the Gallup surveys include on an annual basis. In previous literature in psychology (Yang, 2006;[START_REF] Devos | Conflicts Among Human Values and Trust in Institutions[END_REF][START_REF] Newton | Confidence in Public Institutions. Disaffected democracies[END_REF]Norris, 2000, Buriak et al., 2019) as well as in economics [START_REF] Stevenson | Trust in Public Institutions over the Business Cycle[END_REF], researchers have established that trust in different general institutions is related.

Thus, when trust in general institutions, such as Congress, increases, it should imply increased trust in banks too. With this second instrument, we can capture general trust in institutions.

In Table 9, the first column provides the first-stage regression, and the next three columns summarize the regressions with trust in banks instrumented by electoral turnout and trust in Congress. The first-stage regression confirms a relationship between the instruments and the instrumented variable. An increase in electoral turnout is associated with a decrease in trust in banks; an increase in trust in Congress increases trust in banks. Both coefficients are significant, and the opposing signs affirm that the two variables measure different aspects of trust, as also confirmed by the high R² and significant F-test.

In the second stage, we instrument Trust in Banks using the residuals from the first regression, then estimate the impact of the instrumented variable on overall, on-, and off-balance sheet liquidity creation. In support of our predictions, the relationship between the instrumented variable and liquidity creation overall is positive and significant. The magnitude is slightly higher but close to the value in our main model. The model is also correctly specified, with a high R², significant F-test, and nonsignificant Hansen test. The relationship is positive and significant for on-balance sheet liquidity creation too. We do not observe a significant estimate for off-balance sheet liquidity creation, but the sign of the coefficient is positive, a difference from our main results that may stem from the choice of instruments.

Conclusions

Liquidity creation is one of the two key functions of banks and essential for a well-functioning economy. Many factors might explain its extent, but the role of trust seems central. Trust enables banks to collect deposits and provide long-term lending, and this essential role has been emphasized in substantial theoretical literature (e.g., [START_REF] Diamond | Bank Runs, Deposit Insurance, and Liquidity[END_REF][START_REF] Diamond | Liquidity Risk, Liquidity Creation, and Financial Fragility: A Theory of Banking[END_REF]. But those theoretical models predict two contrasting effects. A positive trust environment might foster liquidity creation, by reinforcing banks' deposit stability, reducing liquidity mismatch costs, lowering the risk of runs, and encouraging banks to take additional risks [START_REF] Diamond | Bank Runs, Deposit Insurance, and Liquidity[END_REF][START_REF] Allen | Financial Intermediaries and Markets[END_REF][START_REF] Repullo | Capital Requirements, Market power, and Risk-Taking in Banking[END_REF]. But higher trust also might hinder banks' liquidity creation, by reducing their incentives to signal trustworthiness [START_REF] Diamond | Liquidity Risk, Liquidity Creation, and Financial Fragility: A Theory of Banking[END_REF][START_REF] Myers | The Paradox of Liquidity[END_REF][START_REF] Flannery | Debt Maturity and the Deadweight Cost of Leverage: Optimally Financing Banking Firms[END_REF].

We find that a trustworthy environment promotes liquidity creation, overall, on-, and offbalance sheets. We rule out time effects, by using quarterly fixed effects at the bank level. In subsample analyses, we highlight that this effect is stronger for small banks, state-chartered banks, and during economic downturns. That is, trust is needed more when interpersonal relations are crucial. With robustness tests to check for potential flaws in our approach, we also address granularity and endogeneity issues. Furthermore, our instrumental variable regression helps isolate exogenous variations in trust that affect liquidity creation. Our consistent results confirm the positive relationship of trust and liquidity creation.

Thus, we address a critical question about the role of trust for banks' core functions. Recent literature suggests the role of norms and values in the economy as key drivers of economic processes and agents' ability to interact. Our approach affirms the impact of societal values on the banking system and provide meaningful insights, as well as policy recommendations.

Liquidity creation is critical to any economy, as a necessary service for depositors and entrepreneurs, but also as a source of economic growth. We provide empirical evidence of a positive relationship between liquidity creation and trust in banks, so policies designed to foster

Table 1. Distribution of Responses

This table displays the distribution of responses to the question, "Please tell me how much confidence you, yourself, have in banks-a great deal, quite a lot, some or very little?" by year. We assigned a score to each modality of the question: none = 0, very little = 1, some = 2, quite a lot = 3, and great deal = 4. Then we took a weighted average of the answers.

Year

Table 7. Business Cycle

For these panel fixed effect regressions, the dependent variable is listed in the second row. LC = liquidity creation, LC_BS = on-balance sheet liquidity creation, LC_OFF = off-balance sheet liquidity creation, and GTA = gross total assets. Each liquidity creation variable is scaled by GTA. The t-statistic level is reported in parentheses. Significance levels of 0.10, 0.05, and 0.01 are indicated by *, **, and ***, respectively. The appendix defines the variables.

Figure 1. Variation of Liquidity Creation and Trust in Banks

This graph plots the average annual (1985-2016) liquidity creation as a percentage (LC/GTA) and the annual trust in banks, measured as the percentage of respondents who state that they trust banks a "great deal." The appendix A defines the variables.

  the estimates of the relationship between Trust in Banks and LC/GTA. Whether we include quarters and bank fixed effects (column 2) or not (column 1), we find a positive relationship between trust and liquidity creation. A 1 percentage point increase in the number of people who report great trust in banks is associated with an increase of 0.169 of the ratio of liquidity creation to total assets. Back-of-envelope calculations indicate the economic effect: Over the study period, Trust in Banks dropped from to 22% in 2006 to 9% in 2010, leading to a 221 bp decrease in liquidity creation in terms of GTA. Mean GTA equals $38.65 million, so in economic terms, a 13 percentage point decrease in Trust in Banks leads to a $854,165 drop in liquidity creation on average at the bank level. The relationship is weaker when we include bank and quarter fixed effects, but it remains positive and significant. Our findings thus add validity to the prediction that improved public trust fosters liquidity creation. Banks can attract depositors more easily and benefit from a stable source of funding, which reduces the risk of maturity mismatches and allows banks to engage more in maturity transformation. If the public trusts banks, they also gain financial strength, by reducing the risk of runs. This outcome tends to allow banks to lend to riskier borrowers and create more liquidity.

  trust in banks should have positive effects on liquidity creation. The basic mechanisms underlying deposit insurance probably relate positively to trust in banks, because depositors can assume banks' stability, so they should be less likely to engage in bank runs. Public policies and prudential regulations, as well as specific communications about these policies, may induce more public trust in banks that can encourage more liquidity creation.Yang K., 2006, Trust and Citizen Involvement Decisions: Trust in Citizens, Trust in Institutions, and Propensity to Trust. Administration & Society 38 (5), 573-595.

Table 2

 2 contains summary statistics, which show that the average LC/GTA is 29.17%, the average LC_BS/GTA is 23,98%, and the average LC_OFF/GTA is 5.19%. Average trust in banks reaches 14%. Figure1details the evolution of trust in banks and liquidity creation, revealing that both trends follow a similar pattern and correlate positively. Liquidity creation steadily increased from 26% to almost 35% over 1985-2004. Then the 2008-2009 financial crisis took its toll, such that liquidity creation fell sharply to 27% in 2009, after which it recovered progressively, to 35% in 2016. Similarly, trust in banks hit the highest rate of 22% in 2006 and sharply decreased to 8% in 2009. Recovery was slower for trust than for liquidity creation, reaching only 11% in 2016. Trust in banks exhibits greater volatility over the period.

Table 2 .

 2 Descriptive StatisticsThis table provides descriptive statistics for the study variables. The appendix contains their definitions.

		Great Deal Quite a Lot Some Very Little	None	No Opinion	Average
	2016	11	16	47	25	1	1	2.13
	2015	12	16	45	24	2	1	2.16
	2014	10	16	43	28	2	0	2.08
	2013	10	16	45	26	2	1	2.10
	2012	9	12	42	33	2	1	1.96
	2011	10	13	40	33	3	1	2
	2010	9	14	45	28	2	1	2.04
	2009	8	14	49	26	3	1	2.04
	2008	11	21	45	21	1	1	2.22
	2007	15	26	44	13	1	1	2.43
	2006	18	31	39	10	0	1	2.58
	2005	22	27	39	11	1	0	2.60
	2004	17	36	36	10	0	1	2.60
	2003	19	31	38	11	1	0	2.58
	2002	17	30	39	12	1	1	2.53
	2001	17	27	41	13	1	1	2.48
	2000	14	32	41	11	1	1	2.5
	1999	16	27	40	15	1	1	2.44
	1998	16	24	43	14	1	2	2.43
	1997	17	24	42	15	1	1	2.43
	1996	16	28	41	13	1	1	2.47
	1995	18	25	42	12	1	2	2.50
	1994	12	23	46	17	0	2	2.30
	1993	14	23	42	19	1	0	2.32
	1992	9	20	44	23	2	1	2.15
	1991	12	20	46	19	2	1	2.25
	1990	13	23	40	21	2	1	2.28
	1989	18	24	38	16	2	2	2.45
	1988	16	33	38	11	1	1	2.55
	1987	15	36	38	10	1	1	2.56
	1986	13	36	37	12	1	1	2.51
	1985	15	36	36	11	1	2	2.56
	1984	20	31	33	14	0	2	2.58

Table 5 .

 5 Size AnalysisFor these panel fixed effect regressions, the dependent variable is listed in the second row. LC = liquidity creation, LC_BS = on-balance sheet liquidity creation, LC_OFF = off-balance sheet liquidity creation, and GTA = gross total assets. Each liquidity creation variable is scaled by GTA. The t-statistic level is reported in parentheses. Significance levels of 0.10, 0.05, and 0.01 are indicated by *, **, and ***, respectively. The appendix defines the variables.

		308.794	261.903	210.898	5.929	6.190	10.182	8.442	2.945	9.354
	R²	0.077	0.066	0.054	0.075	0.078	0.123	0.097	0.036	0.107
	Adjusted R²	-0.016	-0.028	-0.042	-0.136	-0.133	-0.078	-0.049	-0.119	-0.038

A deposit insurance scheme could have a similar effect, by reducing banks' financial fragility and incentives to create liquidity.[START_REF] Fungáčová | Bank Capital, Liquidity Creation and Deposit Insurance[END_REF] explore this possibility but do not find a significant impact.

http://web.mit.edu/cbouwman/www/data.html, last visited on 14/01/2019.

In this calculation, we don't take into account individuals answering "no opinion".

To test the sensitivity of our results, we use the sum of the "great deal" and "quite a lot" responses, and the average index of confidence. Results remain qualitatively similar and are available on request.

Table 3. Correlation Matrix

The table displays pairwise correlations across the main variables. Significance levels of 0.10, 0.05, and 0.01 are indicated by *, **, and ***, respectively.

Table 4. Main Results

For these panel fixed effect regressions, the dependent variable is listed in the second row. LC = liquidity creation, LC_BS = on-balance sheet liquidity creation, LC_OFF = off-balance sheet liquidity creation, and GTA = gross total assets. Each liquidity creation variable is scaled by GTA. Difference between the gross GDP and potential GDP by year. BIS Size Set of dummy variables (small, medium, large), equal to 1 if bank has gross total assets (GTA) up to $1 billion, between $1 and $3 billion, or exceeding $3 billion, respectively; 0 otherwise. [START_REF] Berger | Bank Liquidity Creation[END_REF]


Jérémie D Lebrun

Isabelle Trinsoutrot-Gattin

Marthe Vinceslas-Akpa

Caroline Bailleul

Agathe Brault

Christian Mougin

Karine Laval

Assessing impacts of copper on soil enzyme activities in regard to their natural spatiotemporal variation under long-term different land uses

Keywords: Grassland, Intensive crop, Terrestrial Model Ecosystems, Soil quality, Enzyme, Bioindicators

Enzymes are known to be sensitive indicators of soil quality. Nevertheless, for a relevant interpretation of activity levels, especially in contaminated soils, the natural spatiotemporal variability of enzyme activities needs to be considered. We first monitored the variability of enzymatic activities (acid and alkaline phosphatases, -glucosidase, N-acetyl--glucosaminidase, urease and dehydrogenase) in two agricultural sites in north-western France during a seasonal April -October cycle. For both sites, two types of long-term different agricultural managements, grassland and intensive cropping were considered. Our results revealed great variability of enzymatic activities in space and over time, yet more pronounced for grassland than for cropped soils. Then, we assessed the impact of copper on enzymes activity on Terrestrial Model Ecosystems (TMEs) filled with undisturbed soil, and incubated for 70 days in open-field conditions. Copper was added at two concentration levels corresponding to a regulated annual agronomic input (2 mg kg -1 ) or to a high soil contamination (200 mg kg -1 ). In comparison to effects of natural spatial variability of soil conditions, copper addition did not show significant impacts on enzymatic activities. Finally, our results confirmed that for assessing effective impacts of contaminants in soils under real field conditions, natural spatiotemporal soil variability must be considered.

Introduction

Crop soils are anthropogenic ecosystems, often intensively exploited to meet human needs.

Hence, threatened by the agricultural practices. The use of various organic amendments, the spreading of sewage sludge, as well as application of fertilizers and pesticides contribute to soil contamination by organic or inorganic chemicals. Among contaminants introduced into soil, heavy metals, because of their non-degradability, may have serious consequences with respect to soil biological activity [START_REF] Giller | Toxicity of heavy metals to microorganisms and microbial processes in agricultural soils: a review[END_REF][START_REF] Mikanova | Effects of heavy metals on some soil biological parameters[END_REF]. Copper has been widely used in agricultural practices against fungal or bacterial pests. Furthermore, land application of organic waste is also a potential source of metal contamination [START_REF] Kunito | Copper and zinc fractions affecting microorganisms in long term sludge amended soils[END_REF][START_REF] Madejon | Soil enzymatic response to addition of heavy metals with organic residues[END_REF].

Although the regulated amount of Cu corresponds to 2 mg kg -1 of dry soil for the superficial soil layer (0-10 cm) per year, Cu concentrations may sometimes reach levels exceeding European guidelines of 100 mg kg -1 for agricultural soils [START_REF] Flores-Vélez | Study of the distribution of copper in acid sandy vineyard soil by three different methods[END_REF]. Metal contamination may affect the structure of microbial communities and their functions in terrestrial ecosystems [START_REF] Laguerre | Response of rhizobial populations to mederate copper stress applied to an agricultural soil[END_REF][START_REF] Ranjard | Cumulative effects of short-term polymetal contamination on soil bacterial community structure[END_REF][START_REF] Wang | The influence of soil heavy metals pollution on soil microbial biomass, enzyme activity, and community composition near a copper smelter[END_REF]. For assessment of environmental risk related to metal contamination, the study of relevant bioindicators of soil quality is essential.

During the last decades, several biological endpoints have been proposed to assess soil quality [START_REF] Kandeler | Physiological and biochemical methods for studying soil biota and their fynction[END_REF]. Among these endpoints, enzymes essentially produced by soil microorganisms were mentioned as key-actors of soil behaviour Being involved in reallocation of organic matter, biogeochemical cycles and soil structure (Olander and Vitoussek, 2000;[START_REF] Nannipieri | Microbial diversity and soil functions[END_REF]. Since soil enzymatic activities reflect the structure and functions of microbial communities, they are considered as integrative bioindicators. Hence, soil enzymatic activities are often used for monitoring impacts of soil management, agricultural practices or contaminations on soil health [START_REF] Kandeler | Influence of heavy metals on the functional diversity of soil microbial communities[END_REF][START_REF] Deng | Effect of tillage and residue management on enzyme activities in soils: III. Phosphatases and arylsulfatase[END_REF][START_REF] Gianfreda | Soil enzyme activities as affected by anthropogenic alterations: intensive agricultural practices and organic pollution[END_REF][START_REF] Mamy | No evidence for effect of soil compaction on the degradation and impact of isoproturon[END_REF].

It is generally admitted that high activity levels of enzymes indicate a good soil quality whereas low levels may relate to toxic effects of contaminants on biological processes [START_REF] Kandeler | Influence of heavy metals on the functional diversity of soil microbial communities[END_REF][START_REF] Mikanova | Effects of heavy metals on some soil biological parameters[END_REF]. In literature, metal impacts on enzymatic activities are currently assessed on disturbed soil samples (i.e., sieved and homogenised), then incubated under controlled conditions in the laboratory [START_REF] Ekenler | Effects of trace elements on β-glucosaminidase activity of soils[END_REF][START_REF] Gianfreda | Soil enzyme activities as affected by anthropogenic alterations: intensive agricultural practices and organic pollution[END_REF][START_REF] Demanou | Structural and functional diversity of soil microbial communities as a result of combined applications of copper and mefenoxam[END_REF]. Therefore, results of such experimental approaches are often difficult to interpret in terms of open-field situations. Furthermore, the activity levels of enzymes also depend on different environmental factors such as climate, soil structure, physicochemical characteristics or nutrient availability for microorganisms (Olander and Vitoussek, 2000;[START_REF] Kandeler | Physiological and biochemical methods for studying soil biota and their fynction[END_REF]. Also, since soils often are heterogeneous and contrasting ecosystems, only few studies considered until now, the spatial and temporal variability of enzymatic activities in soils at the plot scale. Usually, such in situ investigations were performed via a limited number of measurements at one single time point [START_REF] Gianfreda | Soil enzyme activities as affected by anthropogenic alterations: intensive agricultural practices and organic pollution[END_REF][START_REF] Bell | Fine scale variability in soil extracellular enzyme is insensitive to rain events and temperature in a mesic system[END_REF]. Since environmental or anthropogenic factors affect the metabolism of microorganism, it is necessary to integrate such confounding factors when interpreting enzymatic indicators to assess the effective impact of contaminants on soil behaviour in real field conditions.

The originality of this present study was to combine experimental approaches performed in situ and in Terrestrial Model Ecosystems (TMEs) in order to assess the real impact of Cu on soil quality and behaviour at the plot scale. For that, we first determined the spatiotemporal variability of enzymatic activities in soils under different agricultural managements (grassland and intensive crop) in the spring, summer and autumn, thus miming a seasonal cycle. Then, the metal impact on soil enzymatic activities was monitored in TMEs containing undisturbed surface soil samples which were incubated in open-field conditions. Two Cu concentrations were selected: 2 mg kg -1 as a regulated agronomic contribution, and 200 mg kg -1 corresponding to consistent soil contamination.

A global indicator of soil microbial activity, the dehydrogenase, as well as five hydrolase activities involved in the biogeochemical P, C and N cycles: acid and alkaline phosphatases, β-glucosidase, N-acetyl-β-glucosaminidase, and urease were measured during experiments.

Material and methods

Sites and soil characteristics

Field experiments were conducted at two locations: Saint-Georges-sur-Fontaine and Yvetot.

The soils in these two sites are developed on loess deposits on limestone plateaus in Normandy, and representative for the major agricultural sector in north-western France. The climate was temperate oceanic with a mean annual temperature and rainfall of 10°C and 800 mm, respectively. The soils are Luvisols (IUSS Working Group WRB, 2006), with 18.0% clay, 67.2% silt and 14.8% sand for Saint-Georges-sur-Fontaine, and 18.2% clay, 62.1% silt and 19.7% sand for Yvetot. Selected physicochemical characteristics are presented in Table 1. For each site, two types of agricultural managements were considered: grassland and conventional arable land use. The grasslands were under permanent cover and untilled for more than 25 years. By contrast, arable soils were used for long-term intensive cropping with a rotation of wheat, maize, flax or beat, annually ploughed and fertilized.

Soil sampling

Multiple sampling was carried out in the 0-10 cm surface layer of soils in April, June and October 2005, representing main seasonal variations of enzyme activity. Twenty replicates spaced 25 m x 25 m were taken in each plot in order to account for spatial variability of the plots. For each replicate, 10 soil cores were collected from a circle of 1-m diameter, and mixed to constitute a composite sample. Each composite sample (3-4 kg) was sieved (< 2 mm) and stored at 4°C prior to measurement of enzyme activities, performed less than one week after sampling.

Assays of enzymatic activities

Analysis of dehydrogenase activity (DH; E.C. 1.1.1.1) was adapted from [START_REF] Schaefer | L'activité déshydrogénasique comme mesure de l'activité biologique globale des sols[END_REF]. In 30-mL centrifuge tubes, 5 g of sieved soil were mixed with 24 ml of distilled water and 1 ml of substrate at 5% TTC (1,3,5-triphenyltetrazolium chloride). Controls were prepared without TTC.

After incubation at 25°C for 16 h, 20 ml of acetone were added and the mixture was kept in the dark for 2 h and shaked every hour. Finally, TTC was added to controls and all mixtures were centrifuged at 6,000 g for 5 min. The absorbance of the supernatant (red colour in case of formazan formed, TPF) was measured at 485 nm. Results were expressed in nmoles TPF g -1 of dry soil h -1 .

Activities of acid phosphatase (PAC; E.C. 3.1.3.2), alkaline phosphatase (PAL; E.C.

3.1.3.1), -glucosidase (GLU; E.C. 3.2.1.21) and N-acetyl--glucosaminidase (NAG; E.C.3.2.1.30)

were measured using their respective substrate of conjugated para-nitrophenyl (Sigma-Aldrich) according to a method adapted from [START_REF] Dick | Soil enzyme activities and biodiversity measurements as integrative microbiological indicators[END_REF]. Assays were carried out in 30-ml centrifuge tubes by mixing 1 g of fresh soil, 4 ml of universal buffer (MUB, pH 6; except for PAL, pH 11) and 1 ml of substrate solution (0.05 M), which was omitted in the control samples. After incubation at 25°C for 2 h, 1 ml of CaCl2 (0.5 M) and 4 ml of NaOH (0.5 M) were added to stop the reaction. At this step, 1 ml of substrate solution was added in control samples. All samples were centrifuged at 12,000 g for 3 min. The p-nitrophenol (PNP) liberated by enzymatic hydrolysis was determined by UV spectrophotometer at 405 nm. Results were expressed in µmoles PNP g -1 of dry soil h -1 . Urease activity (URE; EC 3.5.1.5) was measured according to [START_REF] Kandeler | Short-term assay of soil urease activity using colorimetric determination of ammonium[END_REF] introducing some minor modifications. One gram of fresh soil was dissolved in 50 ml of water and shaken for 10 min. Aliquots of the soil solution (500 µl) were supplemented with 100 µl of 0.4 M urea, which was omitted in the controls. The mixtures were vigorously shaken and incubated at 25°C for 4 h. Then, 100 µl of a salicylate solution (VWR, Hach reagent n°23952-66, 1 bag dissolved in 1 ml of water) were added and the reaction was sustained for 3 min. Finally, 100 µl of a cyanurate solution (VWR, Hach reagent n°23954-66, 1 bag dissolved in 1 ml water) were added. At this step, controls were supplemented with urea solution. After 30 min, the mixtures were centrifuged at 10,000 g for 2 min. The absorbance of the supernatant (blue colour in case of N-NH4 + released) was measured at 610 nm. Results were expressed in nmoles N-NH4 + g -1 of dry soil h -1 .

Impact of copper on soil enzyme activities

Terrestrial Model Ecosystems (TMEs, PVC cylinders of 12.5 cm diameter and 12.5 cm deep) were filled with undisturbed soil columns from 5 representative locations in grassland or cropped soils in the plots of Yvetot. TMEs were incubated in conditions of the open-field for 70 days. Three conditions were examined: control without added Cu, a regulated annual contribution of Cu (2 mg kg -1 ) and a high Cu contamination level of 200 p mg kg -1 . TMEs were spiked with 25 ml of the corresponding concentrated CuSO4 solution (VWR, > 99% purity). Soil pH was found not modified by spiking with CuCO4. Control TMEs received 25 ml of water. For each treatment, enzymatic activities were measured as previously described, from five TMEs after 7, 35 and 70 days of incubation. The representativeness of TMEs was checked by simultaneous measuring of soil enzymatic activities in TMEs and in the plots of Yvetot at the beginning and the end of incubation.

Statistics

All statistical tests were performed with R software (RDevelopmentCoreTeam, 2004).

Analysis of variance (ANOVA) was applied on the datasets to identify (i) the respective importance of agricultural management, localization of sites or sampling date on soil enzymatic activities measured in situ and (ii) the respective importance of agricultural management, TME establishment, incubation time or impact of copper on enzymatic activities measured in TMEs. Previously, normality and homogeneity of variances were checked using Wilk-Shapiro test and Bartlett test, respectively. The significant differences between uncontaminated and contaminated TMEs were determined by Mann Whitney test with a significance level of 5%.

Results

Variability of enzymatic activities in grassland and cropped soils

Results of spatiotemporal variability of soil enzymatic activities in the two sites are presented in Fig. 1. Overall, the activity levels of enzymes were 2 to 4 times higher in grassland soils in comparison to cropped soils, for both sites.

For Yvetot (Fig. 1A.), the enzymes with highest spatiotemporal variability were acid and alkaline phosphatases, β-glucosidase, and to a lesser extent, dehydrogenase. Spatiotemporal variability of enzymatic activities was generally greater in grassland soils. For example, 50% values of acid phosphatase and β-glucosidase activities ranged from 1.85 to 1.19 and from 1.15 to 0.61 µmolPNP h -1 g -1 , respectively, in the grassland soils, and from 0.55 to 0.32 and from 0.41 to 0.19 µmolPNP h -1 g -1 in the soil under intensive cropping. Both the activity level and variability of urease are similar whatever the type of agricultural management of the site.

For Saint-Georges-sur-Fontaine (Fig. 1B.), the highest spatiotemporal variability was observed for acid phosphatase with 50% values ranging from 1.53 to 0.74 µmolPNP h -1 g -1 in grassland and from 0.69 to 0.16 µmolPNP h -1 g -1 in cropped soils. Among tested enzymes, dehydrogenase was most little affected by the type of agricultural management of the site. Overall, the variability of soil enzymatic activities was lower than those measured in the Yvetot site. 2) showed that the agricultural management was the major factor explaining the variability of acid phosphatase and N-acetyl-β-glucosaminidase activities (66 and 42%, respectively). The variability of urease and to a lesser extent of βglucosidase, was related to the sampling date (93 and 36% respectively). None of the three factors explained the variability of alkaline phosphatase or dehydrogenase.

Results of analysis of variance (Table

Impact of copper on soil enzymatic activities

The response of enzymatic activities to Cu was assessed in TMEs with undisturbed samples from Yvetot, incubated for 70 days in open-field conditions. Results are presented in Fig. 2 for grassland, and in Fig. 3 for the cropped soil. Soil enzymatic activities measured both in uncontaminated or contaminated TMEs varied over time. The variations of enzymatic activities were within the range of spatiotemporal variability measured on disturbed soil samples (Fig. 1).

For the grassland TMEs (Fig. 2), the addition of Cu did not lead to significant effects on the activity levels of enzymes, except for acid phosphatase. A decrease in its activity occurred after 7 days of incubation for exposure at both Cu concentration levels. Only the effect of Cu at 200 mg kg - 1 persisted during the incubation time. For the TMEs of crop soil (Fig. 3), only acid phosphatase was inhibited by Cu at 2 mg kg -1 and two enzymatic activities were differentially affected by Cu at 200 mg kg -1 after 70 days of incubation. Indeed, dehydrogenase activity was inhibited by Cu whereas that of N-acetyl-β-glucosaminidase was stimulated by the presence of copper.

Acidic and alkaline phosphatases, β-glucosidase and N-acetyl-β-glucosaminidase activities were measured both in TMEs and in situ at the site of Yvetot, at the start and the end of TMEs incubation. For grassland, no significant difference of enzymatic variations was observed between TMEs and in situ, thus validating our approach of using TMEs for assessing the impact of Cu on soil enzymatic activities. For the cropped soil, only acid phosphatase activity was 1.5 times higher in the plot than in the TMEs (data not shown but integrated in the analysis of variance, Table 3) Results of analysis of variance (Table 3) confirmed that the absence of effects of establishing TMEs on enzymatic activities. Agricultural management was the major factor explaining the variability of N-acetyl-β-glucosaminidase activity (49%), and the incubation time for urease (89%) and for acid phosphatase (47%). The variability of enzymatic activity was only little related to addition of Cu in TMEs (< 20%).

Discussion

Spatiotemporal variability of enzymatic activities in soils

The first objective of our work was to assess the spatial (20 replicates spaced 25 m x 25 m) and temporal (3 sampling dates during different seasons) variations of enzymatic activities in soils under different agricultural management. The activity levels of various enzymes, involved in the main nutrient cycles, were generally 2-4 times higher in grassland than in cropped soils, in both study sites. Such finding was ascribed to the total contents of carbon and nitrogen of the plots, 2-3 times higher in grasslands than in cropped soils, illustrating the effect of an intensive arable land use leading to decreasing nutrient stocks (Table 1). Such correlations between enzymatic activities and physicochemical soil characteristics of nutrient stocks were previously reported for phosphatases or β-glucosidase in literature [START_REF] Deng | Effect of tillage and residue management on enzyme activities in soils: III. Phosphatases and arylsulfatase[END_REF][START_REF] Gianfreda | Soil enzyme activities as affected by anthropogenic alterations: intensive agricultural practices and organic pollution[END_REF][START_REF] Kandeler | Physiological and biochemical methods for studying soil biota and their fynction[END_REF]. In our work, the agricultural management was shown as a major factor explaining the variability of acid phosphatase and N-acetyl-β-glucosaminidase contrarily to other enzymatic activities studied. . Whatever the agricultural management of soils, soil enzymes showed a high spatial variability at the plot scale, consistent with an often great heterogeneous distribution of soil structure and composition over short distances. Indeed, soil represent a congregation of mineral and organic aggregates forming various microhabitats for microorganisms, in which the fluxes of nutrient elements, air and water influence the structure of communities and their functions [START_REF] Nannipieri | Microbial diversity and soil functions[END_REF][START_REF] Calbrix | Analysis of the potential functional diversity of the bacterial community in soil: a reproducible procedure using sole-carbon-source utilization profiles[END_REF][START_REF] Kandeler | Physiological and biochemical methods for studying soil biota and their fynction[END_REF]. Despite the high intra-plot variability, the statistical analyses did not reveal site effects on in situ measured enzymatic activities. This can be explained by the similarity of pedo-climatic characteristics between the sites of Yvetot and Saint-Georges-sur-Fontaine, an encouraging finding for the robustness of such enzymatic tools.

Although soil moisture was highly correlated with the sampling date (R > 0.95), only urease activity varied greatly over time at the plot scale, also observed during the incubation of TMEs (> 93%). It is well-known that urease is regulated by various factors including the climate [START_REF] Krajewska | Ureases I. Functional, catalytic and kinetic properties: A review[END_REF]. Seasonal variations control the biological processes involved in the fluxes of ammonium and agricultural practises such as applications of chemical or organic fertilizers. Little sensitivity of all other enzymes to seasonal variations is consistent with findings of [START_REF] Bell | Fine scale variability in soil extracellular enzyme is insensitive to rain events and temperature in a mesic system[END_REF] who showed that various hydrolase activities including phosphatase, N-acetyl-β-glucosaminidase and βglucosidase, were not affected by rain events and temperature during one year.

Effect of Cu on soil enzymatic activities

One original aspect of our work was the establishment of TMEs incubated in open-field conditions and affected by real climatic variations. In literature, effects of contaminants on enzyme activity was often studied on disturbed soil samples after sieving, and then incubated under controlled conditions [START_REF] Ekenler | Effects of trace elements on β-glucosaminidase activity of soils[END_REF][START_REF] Gianfreda | Soil enzyme activities as affected by anthropogenic alterations: intensive agricultural practices and organic pollution[END_REF][START_REF] Demanou | Structural and functional diversity of soil microbial communities as a result of combined applications of copper and mefenoxam[END_REF].

Sieving of soil samples enhances reallocation of organic carbon, and favours bioavailability of organic C to soil microorganisms, stimulating their development and growth. However, it is known that the sensitivity of the enzymatic responses to metals depends on the physiological status in fungi, which constitute one of the largest biomasses in soils [START_REF] Lebrun | Insights into the development of fungal biomarkers for metal ecotoxicity assessment: case of Trametes versicolor exposed to copper[END_REF]. In this study, we chose to compose TMEs from undisturbed soil to preserve the soil structure, and interactions between different soil and the metabolic status of soil microorganisms. Thus, our experimental approach enables to mime environmental conditions. The relevance of this approach for predicting what happens in the field was confirmed by comparing enzymatic activities measured in TMEs and in situ.

Although Cu was added in TMEs to a concentration 100 times higher than regulated contents per year, enzymatic activities were only little affected by the contaminant, independent of the type of considered soil management. Only acid phosphatase was inhibited by Cu at 200 mg kg -1 in TMEs of grassland soils of Yvetot. In TMEs of cropped soil, N-acetyl-β-glucosaminidase was stimulated and dehydrogenase was inhibited by Cu at 200 mg kg -1 but only after 70 days of incubation in open-field conditions. Therefore, no statistical links were established between the activity levels of enzymes and the presence of copper in grassland or cropped soils when comparing to effects related to natural spatiotemporal variations. Our results corroborate findings of studies that also reported the absence of metal impacts on biological endpoints [START_REF] Avidano | Characterization of soil health in an Italian polluted site by using microorganisms as bioindicators[END_REF][START_REF] Gianfreda | Soil enzyme activities as affected by anthropogenic alterations: intensive agricultural practices and organic pollution[END_REF][START_REF] Demanou | Structural and functional diversity of soil microbial communities as a result of combined applications of copper and mefenoxam[END_REF]. By contrast, several other studies have mentioned a sensitivity of enzymatic activities measured in soil samples collected from in situ multiple metal contaminations including Cu: β-glucosidase [START_REF] Hinojosa | Soil moisture pre-treatment effects on enzyme activities as indicators of heavy metal-contaminated and reclaimed soils[END_REF], alkaline phosphatase [START_REF] Wang | The influence of soil heavy metals pollution on soil microbial biomass, enzyme activity, and community composition near a copper smelter[END_REF], urease and dehydrogenase activities [START_REF] Mikanova | Effects of heavy metals on some soil biological parameters[END_REF]). Yet, in the latter studies, it was difficult to discriminate the contribution of each metal to the response of enzymatic activities.

Effects of metals in soils on biological endpoints depend rather on their bioavailability than on total concentrations [START_REF] Van Gestel | Physico-chemical and biological parameters determine metal bioavailability in soils[END_REF]. Metal bioavailability is highly related to different intrinsic soil properties such as the nature and contents of organic matter, the soil's moisture content or the ionic composition… In our work, we also measured the EDTA-extractable Cu concentrations in TMEs. For the addition of Cu at 200 mg kg -1 in TMEs, we obtained about 105 mg kg -1 of Cu, i.e. about 50% of bioavailable Cu, whatever the agricultural management (data not shown here). Yet these data confirm that Cu was available for soil microorganisms without modifying the global levels of enzymatic activities. Although Cu is an essential element for all biota, a high Cu input in soil may inducetoxic effects on microorganisms or affect biodiversity by acting as a factor of species selection. For example, Cu may enhance changes in the structure of microbial communities [START_REF] Laguerre | Response of rhizobial populations to mederate copper stress applied to an agricultural soil[END_REF][START_REF] Wang | The influence of soil heavy metals pollution on soil microbial biomass, enzyme activity, and community composition near a copper smelter[END_REF]. The fact that these enzymatic tools do not reflect a change in the structure of microbial communities and metal impact on their functions could be explained by a functional redundancy, i.e. the enzymes tested in this study are commonly present in all microorganisms.

Conclusion

Our work provides a first dataset on soil enzymatic activities for an important agricultural region on north-western France. We showed that activity levels of different soil enzymes involved in soil behaviour and major nutriment cycling were subject to great natural spatiotemporal variations at the plot scale. Such natural variability of enzyme activity should be considered interpretating their responses to different human pressures, such as agricultural land management Their activity levels were found generally higher in grassland soils than in soils under intensively arable land use, which was related to the nutrient contents of the soils. However, enzymatic activities were revealed as no good descriptors for contamination of soils by Cu, in regard to natural soil variability. Our work underlined the need of considering spatiotemporal variations of biological endpoints when assessing effective impact of contaminants in real field situations.

Table 1 .

 1 Soil physicochemical characteristics of two agricultural sites, Yvetot and Saint-Georges sur-Fontaine, under different agricultural managements. Bold values are means. Table 2. Effects of agricultural management, sampling date and localization of sites on enzymatic activities measured in situ by ANOVA: explanatory factors of the variabilityin %. Bold values are the major factors of variability for each enzyme. Table 3. Effects of agricultural management, TME establishment, incubation time and copper impact on enzymatic activities measured from undisturbed soil TMEs by ANOVA: explanatory factors of the variability in %. Bold values are the major factors of variability for each enzyme.

Fig. 1 .

 1 Fig. 1. Spatiotemporal variability of enzymatic activities in Yvetot (A) and Saint-Georges-sur-

Fig. 2 .

 2 Fig. 2. Enzymatic activities in TMEs filled with undisturbed soil of grassland and incubated in

Fig. 3 .

 3 Fig. 3. Enzymatic activities in TMEs filled with undisturbed soil under intensive crops and

Acknowledgments

The authors are grateful to the French Agency for Environment and Energy Management (ADEME) and the region of Haute-Normandie (France) for their financial support. They thank Folkert van Oort (INRA Versailles) for all the remarks he made for the improvement of this article.