B Appendix: Tables and Figures

<table><thead><tr><th>Stylized facts</th><th>Empirical studies (among others)</th></tr></thead><tbody><tr><td><strong>Macroeconomic stylized facts</strong><br> SF1 Endogenous self-sustained growth with persistent fluctuations<br> SF2 Fat-tailed GDP growth-rate distribution<br> SF3 Recession duration exponentially distributed<br> SF4 Relative volatility of GDP, consumption, investments and debt<br> SF5 Cross-correlations of macro variables<br> SF6 Pro-cyclical aggregate R&amp;D investment<br> SF7 Cross-correlations of credit-related variables<br> SF8 Cross-correlation between firm debt and loan losses<br> SF9 Cross-correlation financial aggregates</td><td>Burns and Mitchell (1946); Kuznets and Murphy (1966)<br> Zarnowitz (1985); Stock and Watson (1999)<br> Fagiolo et al. (2008); Castaldi and Dosi (2009)<br> Ausloos et al. (2004); Wright (2005)<br> Stock and Watson (1999); Napoletano et al. (2006)<br> Stock and Watson (1999); Napoletano et al. (2006)<br> Wälde and Woitek (2004)<br> Lown and Morgan (2006); Leary (2009)<br> Foos et al. (2010)<br> Adrian et al. (2013); Zoltan and Kumhof (2015)</td></tr><tr><td><strong>Interbank network stylized facts</strong><br> SF10 Disassortativity<br> SF11 Centrality-bank size relation<br> SF12 Heterogeneous interbank network density<br> SF13 Large (small) banks are net borrowers (lenders)</td><td>Soramäki et al. (2007); Cocco et al. (2009)<br> Craig and von Peter (2014); Fricke and Lux (2015)<br> Alves et al. (2013), Aldasoro and Alves (2018)<br> Müller (2006); Liu et al. (2020)</td></tr><tr><td><strong>Microeconomic stylized facts</strong><br> SF14 Firm (log) size distribution is right-skewed<br> SF15 Fat-tailed firm growth-rate distribution<br> SF16 Productivity heterogeneity across firms<br> SF17 Persistent productivity differential across firms<br> SF18 Lumpy investment rates at firm-level</td><td>Dosi (2005)<br> Bottazzi and Secchi (2003, 2006)<br> Bartelsman and Doms (2000); Dosi (2005)<br> Bartelsman and Doms (2000); Dosi (2005)<br> Doms et al. (1998)</td></tr></tbody></table>

Notes: The table reports the full list of stylized facts that our model is able to replicate. In *italics*, we highlight those that are relative to our version.

<caption>**Table 4:** Main empirical stylized facts replicated by the model.</caption>
<table><caption>**Table 5:** Main parameters and initial conditions in the economic system. For previous parametrization of some sub-portions of the model and for model sensitivity to key parameters see Dosi et al. (2010, 2015); Lamperti et al. (2018).</caption><thead><tr><th>Description</th><th>Symbol</th><th>Value</th></tr></thead><tbody><tr><td>Monte Carlo replications</td><td>MC</td><td>1000</td></tr><tr><td>Time sample in economic system</td><td>T</td><td>500</td></tr><tr><td>Transient period (time sample)</td><td>T</td><td>300</td></tr><tr><td>Number of firms in capital-good industry</td><td>F<sub>1</sub></td><td>60</td></tr><tr><td>Number of firms in consumption-good industry</td><td>F<sub>2</sub></td><td>300</td></tr><tr><td>Number of bank</td><td>B</td><td>20</td></tr><tr><td>Capital-good firms' mark-up</td><td>μ<sub>1</sub></td><td>0.04</td></tr><tr><td>Consumption-good firm initial mark-up</td><td>μ̄<sub>0</sub></td><td>0.17</td></tr><tr><td>Initial bank mark-up</td><td>μ<sup>deb</sup><sub>0</sub></td><td>0.05</td></tr><tr><td>Uniform distribution supports</td><td>[φ<sub>1</sub>, φ<sub>2</sub>]</td><td>[0.10, 0.90]</td></tr><tr><td>Wage setting ΔĀB weight</td><td>ψ<sub>1</sub></td><td>1</td></tr><tr><td>Wage setting Δcpi weight</td><td>ψ<sub>2</sub></td><td>0.05</td></tr><tr><td>Wage setting ΔU weight</td><td>ψ<sub>3</sub></td><td>0.05</td></tr><tr><td>R&D investment propensity (industrial)</td><td>ν</td><td>0.04</td></tr><tr><td>R&D allocation to innovative search</td><td>ξ</td><td>0.5</td></tr><tr><td>Firm search capabilities parameters</td><td>ζ<sub>1,2</sub></td><td>0.3</td></tr><tr><td>R&D investment propensity (energy)</td><td>ξ<sub>e</sub></td><td>0.01</td></tr><tr><td>Beta distribution parameters (innovation)</td><td>(α<sub>1</sub>, β<sub>1</sub>)</td><td>(3, 3)</td></tr><tr><td>Beta distribution support (innovation)</td><td>[χ<sub>1</sub>, χ̄<sub>1</sub>]</td><td>[-0.2, 0.2]</td></tr><tr><td>Desired inventories</td><td>l</td><td>0.1</td></tr><tr><td>Physical scrapping age (industrial)</td><td>η</td><td>20</td></tr><tr><td>Payback period (industrial)</td><td>b</td><td>3</td></tr><tr><td>Proxy of bank's capital adequacy (fixed by regulator)</td><td>τ<sup>B</sup></td><td>0.045</td></tr><tr><td>Markdown on bank deposits interest rate</td><td>md<sup>dep</sup></td><td>0.78</td></tr><tr><td>Markdown on central bank deposits interest rate</td><td>md<sup>res</sup></td><td>0.41</td></tr><tr><td>Markdown on interbank interest rate</td><td>md<sup>IB</sup></td><td>0.31</td></tr><tr><td>Markdown on government bonds</td><td>md<sup>bonds</sup></td><td>0</td></tr><tr><td>Recovery rate on interbank claims</td><td>ρ<sup>IB</sup></td><td>0</td></tr><tr><td>Average idiosyncratic shock to haircut</td><td>h<sub>0</sub></td><td>0.2</td></tr><tr><td>Sensitivity to inflation gap (Taylor rule)</td><td>γ<sub>π</sub></td><td>1.1</td></tr><tr><td>Sensitivity to unemployment gap (Taylor rule)</td><td>γ<sub>U</sub></td><td>1.1</td></tr></tbody></table>
<table><caption>**Table 5:** Main parameters and initial conditions in the economic system. For previous parametrization of some sub-portions of the model and for model sensitivity to key parameters see Dosi et al. (2010, 2015); Lamperti et al. (2018).</caption><thead><tr><th>Description</th><th>Symbol</th><th>Value</th></tr></thead><tbody><tr><td>Monte Carlo replications</td><td>MC</td><td>1000</td></tr><tr><td>Time sample in economic system</td><td>T</td><td>500</td></tr><tr><td>Transient period (time sample)</td><td>T</td><td>300</td></tr><tr><td>Number of firms in capital-good industry</td><td>F<sub>1</sub></td><td>60</td></tr><tr><td>Number of firms in consumption-good industry</td><td>F<sub>2</sub></td><td>300</td></tr><tr><td>Number of bank</td><td>B</td><td>20</td></tr><tr><td>Capital-good firms' mark-up</td><td>μ<sub>1</sub></td><td>0.04</td></tr><tr><td>Consumption-good firm initial mark-up</td><td>μ̄<sub>0</sub></td><td>0.17</td></tr><tr><td>Initial bank mark-up</td><td>μ<sup>deb</sup><sub>0</sub></td><td>0.05</td></tr><tr><td>Uniform distribution supports</td><td>[φ<sub>1</sub>, φ<sub>2</sub>]</td><td>[0.10, 0.90]</td></tr><tr><td>Wage setting ΔĀB weight</td><td>ψ<sub>1</sub></td><td>1</td></tr><tr><td>Wage setting Δcpi weight</td><td>ψ<sub>2</sub></td><td>0.05</td></tr><tr><td>Wage setting ΔU weight</td><td>ψ<sub>3</sub></td><td>0.05</td></tr><tr><td>R&D investment propensity (industrial)</td><td>ν</td><td>0.04</td></tr><tr><td>R&D allocation to innovative search</td><td>ξ</td><td>0.5</td></tr><tr><td>Firm search capabilities parameters</td><td>ζ<sub>1,2</sub></td><td>0.3</td></tr><tr><td>R&D investment propensity (energy)</td><td>ξ<sub>e</sub></td><td>0.01</td></tr><tr><td>Beta distribution parameters (innovation)</td><td>(α<sub>1</sub>, β<sub>1</sub>)</td><td>(3, 3)</td></tr><tr><td>Beta distribution support (innovation)</td><td>[χ<sub>1</sub>, χ̄<sub>1</sub>]</td><td>[-0.2, 0.2]</td></tr><tr><td>Desired inventories</td><td>l</td><td>0.1</td></tr><tr><td>Physical scrapping age (industrial)</td><td>η</td><td>20</td></tr><tr><td>Payback period (industrial)</td><td>b</td><td>3</td></tr><tr><td>Proxy of bank's capital adequacy (fixed by regulator)</td><td>τ<sup>B</sup></td><td>0.045</td></tr><tr><td>Markdown on bank deposits interest rate</td><td>md<sup>dep</sup></td><td>0.78</td></tr><tr><td>Markdown on central bank deposits interest rate</td><td>md<sup>res</sup></td><td>0.41</td></tr><tr><td>Markdown on interbank interest rate</td><td>md<sup>IB</sup></td><td>0.31</td></tr><tr><td>Markdown on government bonds</td><td>md<sup>bonds</sup></td><td>0</td></tr><tr><td>Recovery rate on interbank claims</td><td>ρ<sup>IB</sup></td><td>0</td></tr><tr><td>Average idiosyncratic shock to haircut</td><td>h<sub>0</sub></td><td>0.2</td></tr><tr><td>Sensitivity to inflation gap (Taylor rule)</td><td>γ<sub>π</sub></td><td>1.1</td></tr><tr><td>Sensitivity to unemployment gap (Taylor rule)</td><td>γ<sub>U</sub></td><td>1.1</td></tr></tbody></table>
<table><thead><tr><th rowspan="2"></th><th colspan="10">Macro-prudential policy parameter τ<sup>B</sup></th></tr><tr><th>2%</th><th>2.5%</th><th>3%</th><th>3.5%</th><th>4%</th><th>4.5%</th><th>5%</th><th>5.5%</th><th>6%</th><th>6.5%</th><th>7%</th></tr></thead><tbody><tr><td rowspan="4">Loss contagion</td><td>BASE</td><td>-0.17</td><td>-0.162</td><td>-0.206**</td><td>-0.091</td><td>-0.134</td><td>0</td><td>-0.137</td><td>-0.165*</td><td>-0.146</td><td>-0.133</td><td>-0.023</td></tr><tr><td>SEMI</td><td>-0.18*</td><td>-0.015</td><td>0.076</td><td>-0.091</td><td>-0.142</td><td>-0.168*</td><td>-0.132</td><td>-0.143</td><td>-0.139</td><td>-0.138</td><td>-0.094</td></tr><tr><td>NEVA</td><td>-0.724***</td><td>-0.718***</td><td>-0.743***</td><td>-0.674***</td><td>-0.707***</td><td>-0.733***</td><td>-0.687***</td><td>-0.724***</td><td>-0.747***</td><td>-0.726***</td><td>-0.739***</td></tr><tr><td>NEVA<sup>2</sup></td><td>-0.688***</td><td>-0.694***</td><td>-0.744***</td><td>-0.659***</td><td>-0.732***</td><td>-0.738***</td><td>-0.7***</td><td>-0.69***</td><td>-0.758***</td><td>-0.733***</td><td>-0.706***</td></tr><tr><td rowspan="4">Bank defaults</td><td>BASE</td><td>-0.222*</td><td>-0.216*</td><td>-0.254**</td><td>-0.12</td><td>-0.138</td><td>0</td><td>-0.142</td><td>-0.16</td><td>-0.141</td><td>-0.114</td><td>0.041</td></tr><tr><td>SEMI</td><td>-0.154</td><td>0.023</td><td>0.144</td><td>-0.076</td><td>-0.112</td><td>-0.133</td><td>-0.064</td><td>-0.141</td><td>-0.096</td><td>-0.096</td><td>-0.008</td></tr><tr><td>NEVA</td><td>-0.441***</td><td>-0.455***</td><td>-0.485***</td><td>-0.405***</td><td>-0.434***</td><td>-0.495***</td><td>-0.422***</td><td>-0.448***</td><td>-0.501***</td><td>-0.446***</td><td>-0.477***</td></tr><tr><td>NEVA<sup>2</sup></td><td>-0.421***</td><td>-0.423***</td><td>-0.517***</td><td>-0.358***</td><td>-0.488***</td><td>-0.52***</td><td>-0.426***</td><td>-0.387***</td><td>-0.522***</td><td>-0.473***</td><td>-0.403***</td></tr><tr><td rowspan="4">Bailout costs</td><td>BASE</td><td>-0.24*</td><td>-0.211*</td><td>-0.237**</td><td>-0.114</td><td>-0.147</td><td>0</td><td>-0.153</td><td>-0.182</td><td>-0.158</td><td>-0.137</td><td>-0.001</td></tr><tr><td>SEMI</td><td>-0.165</td><td>0.016</td><td>0.113</td><td>-0.082</td><td>-0.124</td><td>-0.151</td><td>-0.105</td><td>-0.171</td><td>-0.13</td><td>-0.134</td><td>-0.043</td></tr><tr><td>NEVA</td><td>-0.407***</td><td>-0.394***</td><td>-0.446***</td><td>-0.34***</td><td>-0.396***</td><td>-0.431***</td><td>-0.356***</td><td>-0.41***</td><td>-0.455***</td><td>-0.389***</td><td>-0.43***</td></tr><tr><td>NEVA<sup>2</sup></td><td>-0.367***</td><td>-0.357***</td><td>-0.464***</td><td>-0.298***</td><td>-0.431***</td><td>-0.461***</td><td>-0.376***</td><td>-0.346***</td><td>-0.469***</td><td>-0.424***</td><td>-0.374***</td></tr><tr><td rowspan="4">Credit Supply</td><td>BASE</td><td>0.029***</td><td>0.026***</td><td>0.036***</td><td>0.028***</td><td>0.016</td><td>0</td><td>0.014</td><td>0.009</td><td>-0.001</td><td>0.013</td><td>-0.014</td></tr><tr><td>SEMI</td><td>0.016</td><td>0.015</td><td>0.003</td><td>0.01</td><td>0.02**</td><td>0.011</td><td>0.006</td><td>0.008</td><td>-0.004</td><td>0.008</td><td>-0.006</td></tr><tr><td>NEVA</td><td>0.002</td><td>0.004</td><td>-0.001</td><td>-0.003</td><td>-0.013</td><td>0</td><td>-0.005</td><td>-0.015</td><td>-0.006</td><td>-0.023**</td><td>-0.027**</td></tr><tr><td>NEVA<sup>2</sup></td><td>-0.001</td><td>-0.002</td><td>0.005</td><td>-0.005</td><td>-0.003</td><td>0.006</td><td>-0.012</td><td>-0.025**</td><td>-0.008</td><td>-0.024**</td><td>-0.05***</td></tr><tr><td rowspan="4">Total Loans</td><td>BASE</td><td>0.02***</td><td>0.022***</td><td>0.02***</td><td>0.016**</td><td>0.012*</td><td>0</td><td>0.008</td><td>0.002</td><td>-0.002</td><td>0.006</td><td>-0.019**</td></tr><tr><td>SEMI</td><td>0.016**</td><td>0.01</td><td>-0.001</td><td>0.006</td><td>0.011</td><td>0.005</td><td>0</td><td>0.005</td><td>-0.011</td><td>-0.003</td><td>-0.013</td></tr><tr><td>NEVA</td><td>-0.002</td><td>-0.003</td><td>-0.01</td><td>-0.014</td><td>-0.022**</td><td>-0.004</td><td>-0.016*</td><td>-0.023**</td><td>-0.023**</td><td>-0.035***</td><td>-0.043***</td></tr><tr><td>NEVA<sup>2</sup></td><td>-0.007</td><td>-0.005</td><td>-0.003</td><td>-0.011</td><td>-0.014</td><td>-0.004</td><td>-0.023**</td><td>-0.03***</td><td>-0.02**</td><td>-0.035***</td><td>-0.063***</td></tr><tr><td rowspan="4">Credit Demand</td><td>BASE</td><td>0.011**</td><td>0.012**</td><td>0.012**</td><td>0.01**</td><td>0.007</td><td>0</td><td>0.008</td><td>0.004</td><td>0.001</td><td>0.006</td><td>-0.008</td></tr><tr><td>SEMI</td><td>0.009</td><td>0.005</td><td>-0.003</td><td>0.003</td><td>0.008</td><td>0.004</td><td>0.003</td><td>0.005</td><td>-0.004</td><td>0.003</td><td>-0.002</td></tr><tr><td>NEVA</td><td>0.002</td><td>0.005</td><td>0.001</td><td>0</td><td>-0.001</td><td>0.007</td><td>0.002</td><td>-0.001</td><td>0.001</td><td>-0.007</td><td>-0.009</td></tr><tr><td>NEVA<sup>2</sup></td><td>0</td><td>0.002</td><td>0.006</td><td>0.001</td><td>0.002</td><td>0.007</td><td>-0.001</td><td>-0.004</td><td>0.001</td><td>-0.007</td><td>-0.017***</td></tr><tr><td rowspan="4">Fin. constraints</td><td>BASE</td><td>-0.055***</td><td>-0.06***</td><td>-0.046***</td><td>-0.038**</td><td>-0.025</td><td>0</td><td>-0.007</td><td>-0.002</td><td>0.007</td><td>0.001</td><td>0.055***</td></tr><tr><td>SEMI</td><td>-0.044***</td><td>-0.027</td><td>-0.004</td><td>-0.021</td><td>-0.021</td><td>-0.012</td><td>0.008</td><td>0.013</td><td>0.025</td><td>0.022</td><td>0.051**</td></tr><tr><td>NEVA</td><td>-0.018</td><td>-0.007</td><td>0.003</td><td>0.003</td><td>0.029</td><td>-0.01</td><td>0.015</td><td>0.038*</td><td>0.043**</td><td>0.053**</td><td>0.073***</td></tr><tr><td>NEVA<sup>2</sup></td><td>-0.003</td><td>-0.01</td><td>-0.014</td><td>-0.003</td><td>0.011</td><td>-0.011</td><td>0.026</td><td>0.055***</td><td>0.034*</td><td>0.052**</td><td>0.124***</td></tr><tr><td rowspan="4">GDP growth</td><td>BASE</td><td>0.01**</td><td>0.016***</td><td>0.011***</td><td>0.007*</td><td>0.008*</td><td>0</td><td>0.003</td><td>0</td><td>0.002</td><td>0</td><td>-0.01*</td></tr><tr><td>SEMI</td><td>0.009**</td><td>0.007</td><td>0</td><td>0.005</td><td>0.005</td><td>0.002</td><td>-0.003</td><td>0.001</td><td>-0.006</td><td>-0.003</td><td>-0.008</td></tr><tr><td>NEVA</td><td>0.003</td><td>0.001</td><td>0.001</td><td>-0.001</td><td>-0.005</td><td>0.004</td><td>-0.001</td><td>-0.009*</td><td>-0.009*</td><td>-0.013***</td><td>-0.015***</td></tr><tr><td>NEVA<sup>2</sup></td><td>0.002</td><td>0.004</td><td>0.005</td><td>-0.001</td><td>-0.001</td><td>0.004</td><td>-0.004</td><td>-0.012**</td><td>-0.005</td><td>-0.014***</td><td>-0.024***</td></tr><tr><td rowspan="4">Investment growth</td><td>BASE</td><td>0.02**</td><td>0.034***</td><td>0.026***</td><td>0.016**</td><td>0.017**</td><td>0</td><td>0.006</td><td>0.004</td><td>0.005</td><td>0.001</td><td>-0.01</td></tr><tr><td>SEMI</td><td>0.019**</td><td>0.016**</td><td>0.01</td><td>0.013*</td><td>0.012</td><td>0.009</td><td>-0.006</td><td>0.003</td><td>-0.009</td><td>-0.002</td><td>-0.013</td></tr><tr><td>NEVA</td><td>0.006</td><td>0</td><td>0.003</td><td>-0.003</td><td>-0.013</td><td>0.003</td><td>-0.012</td><td>-0.015*</td><td>-0.015</td><td>-0.022**</td><td>-0.023**</td></tr><tr><td>NEVA<sup>2</sup></td><td>0.008</td><td>0.003</td><td>0.003</td><td>-0.006</td><td>-0.014</td><td>0.003</td><td>-0.01</td><td>-0.027***</td><td>-0.012</td><td>-0.033***</td><td>-0.048***</td></tr><tr><td rowspan="4">Unemp. rate</td><td>BASE</td><td>-0.218***</td><td>-0.232***</td><td>-0.195***</td><td>-0.158**</td><td>-0.092</td><td>0</td><td>-0.042</td><td>-0.026</td><td>0.008</td><td>-0.023</td><td>0.195**</td></tr><tr><td>SEMI</td><td>-0.171**</td><td>-0.08</td><td>0.005</td><td>-0.057</td><td>-0.088</td><td>-0.063</td><td>0.034</td><td>-0.046</td><td>0.098</td><td>0.05</td><td>0.158*</td></tr><tr><td>NEVA</td><td>-0.125*</td><td>-0.078</td><td>-0.044</td><td>-0.042</td><td>0.048</td><td>-0.108</td><td>-0.014</td><td>0.06</td><td>0.067</td><td>0.092</td><td>0.188**</td></tr><tr><td>NEVA<sup>2</sup></td><td>-0.054</td><td>-0.077</td><td>-0.109</td><td>-0.079</td><td>-0.023</td><td>-0.104</td><td>0.022</td><td>0.111</td><td>0.032</td><td>0.108</td><td>0.375***</td></tr></tbody></table>

Notes: The table shows, for a set of macroeconomic real and financial indicators, the Monte Carlo average percentage difference between the value of statistic X under the scenario of interest s and a baseline scenario base, $(X_s = X_{base})/X_{base}$. In order to have statistics comparable across different capital requirement regimes, a reference-baseline scenario is fixed at $\tau^B = 0.045$, i.e., the baseline parametrization in Section 3, centered around 0 by construction.

We also test whether the difference in means is statistically significant or not between the two scenarios. The null hypothesis is that there is no statistical difference in the means calculated in the two scenarios: *p<0.1; **p<0.05; ***p<0.01

<caption>Table 6: Macroeconomic effects of NEVA implementation under different capital requirement regimes.</caption>
<table><thead><tr><th rowspan="2"></th><th colspan="10">Macro-prudential policy parameter τ<sup>B</sup></th></tr><tr><th>2%</th><th>2.5%</th><th>3%</th><th>3.5%</th><th>4%</th><th>4.5%</th><th>5%</th><th>5.5%</th><th>6%</th><th>6.5%</th><th>7%</th></tr></thead><tbody><tr><td rowspan="4">Loss contagion</td><td>BASE</td><td>-0.17</td><td>-0.162</td><td>-0.206**</td><td>-0.091</td><td>-0.134</td><td>0</td><td>-0.137</td><td>-0.165*</td><td>-0.146</td><td>-0.133</td><td>-0.023</td></tr><tr><td>SEMI</td><td>-0.18*</td><td>-0.015</td><td>0.076</td><td>-0.091</td><td>-0.142</td><td>-0.168*</td><td>-0.132</td><td>-0.143</td><td>-0.139</td><td>-0.138</td><td>-0.094</td></tr><tr><td>NEVA</td><td>-0.724***</td><td>-0.718***</td><td>-0.743***</td><td>-0.674***</td><td>-0.707***</td><td>-0.733***</td><td>-0.687***</td><td>-0.724***</td><td>-0.747***</td><td>-0.726***</td><td>-0.739***</td></tr><tr><td>NEVA<sup>2</sup></td><td>-0.688***</td><td>-0.694***</td><td>-0.744***</td><td>-0.659***</td><td>-0.732***</td><td>-0.738***</td><td>-0.7***</td><td>-0.69***</td><td>-0.758***</td><td>-0.733***</td><td>-0.706***</td></tr><tr><td rowspan="4">Bank defaults</td><td>BASE</td><td>-0.222*</td><td>-0.216*</td><td>-0.254**</td><td>-0.12</td><td>-0.138</td><td>0</td><td>-0.142</td><td>-0.16</td><td>-0.141</td><td>-0.114</td><td>0.041</td></tr><tr><td>SEMI</td><td>-0.154</td><td>0.023</td><td>0.144</td><td>-0.076</td><td>-0.112</td><td>-0.133</td><td>-0.064</td><td>-0.141</td><td>-0.096</td><td>-0.096</td><td>-0.008</td></tr><tr><td>NEVA</td><td>-0.441***</td><td>-0.455***</td><td>-0.485***</td><td>-0.405***</td><td>-0.434***</td><td>-0.495***</td><td>-0.422***</td><td>-0.448***</td><td>-0.501***</td><td>-0.446***</td><td>-0.477***</td></tr><tr><td>NEVA<sup>2</sup></td><td>-0.421***</td><td>-0.423***</td><td>-0.517***</td><td>-0.358***</td><td>-0.488***</td><td>-0.52***</td><td>-0.426***</td><td>-0.387***</td><td>-0.522***</td><td>-0.473***</td><td>-0.403***</td></tr><tr><td rowspan="4">Bailout costs</td><td>BASE</td><td>-0.24*</td><td>-0.211*</td><td>-0.237**</td><td>-0.114</td><td>-0.147</td><td>0</td><td>-0.153</td><td>-0.182</td><td>-0.158</td><td>-0.137</td><td>-0.001</td></tr><tr><td>SEMI</td><td>-0.165</td><td>0.016</td><td>0.113</td><td>-0.082</td><td>-0.124</td><td>-0.151</td><td>-0.105</td><td>-0.171</td><td>-0.13</td><td>-0.134</td><td>-0.043</td></tr><tr><td>NEVA</td><td>-0.407***</td><td>-0.394***</td><td>-0.446***</td><td>-0.34***</td><td>-0.396***</td><td>-0.431***</td><td>-0.356***</td><td>-0.41***</td><td>-0.455***</td><td>-0.389***</td><td>-0.43***</td></tr><tr><td>NEVA<sup>2</sup></td><td>-0.367***</td><td>-0.357***</td><td>-0.464***</td><td>-0.298***</td><td>-0.431***</td><td>-0.461***</td><td>-0.376***</td><td>-0.346***</td><td>-0.469***</td><td>-0.424***</td><td>-0.374***</td></tr><tr><td rowspan="4">Credit Supply</td><td>BASE</td><td>0.029***</td><td>0.026***</td><td>0.036***</td><td>0.028***</td><td>0.016</td><td>0</td><td>0.014</td><td>0.009</td><td>-0.001</td><td>0.013</td><td>-0.014</td></tr><tr><td>SEMI</td><td>0.016</td><td>0.015</td><td>0.003</td><td>0.01</td><td>0.02**</td><td>0.011</td><td>0.006</td><td>0.008</td><td>-0.004</td><td>0.008</td><td>-0.006</td></tr><tr><td>NEVA</td><td>0.002</td><td>0.004</td><td>-0.001</td><td>-0.003</td><td>-0.013</td><td>0</td><td>-0.005</td><td>-0.015</td><td>-0.006</td><td>-0.023**</td><td>-0.027**</td></tr><tr><td>NEVA<sup>2</sup></td><td>-0.001</td><td>-0.002</td><td>0.005</td><td>-0.005</td><td>-0.003</td><td>0.006</td><td>-0.012</td><td>-0.025**</td><td>-0.008</td><td>-0.024**</td><td>-0.05***</td></tr><tr><td rowspan="4">Total Loans</td><td>BASE</td><td>0.02***</td><td>0.022***</td><td>0.02***</td><td>0.016**</td><td>0.012*</td><td>0</td><td>0.008</td><td>0.002</td><td>-0.002</td><td>0.006</td><td>-0.019**</td></tr><tr><td>SEMI</td><td>0.016**</td><td>0.01</td><td>-0.001</td><td>0.006</td><td>0.011</td><td>0.005</td><td>0</td><td>0.005</td><td>-0.011</td><td>-0.003</td><td>-0.013</td></tr><tr><td>NEVA</td><td>-0.002</td><td>-0.003</td><td>-0.01</td><td>-0.014</td><td>-0.022**</td><td>-0.004</td><td>-0.016*</td><td>-0.023**</td><td>-0.023**</td><td>-0.035***</td><td>-0.043***</td></tr><tr><td>NEVA<sup>2</sup></td><td>-0.007</td><td>-0.005</td><td>-0.003</td><td>-0.011</td><td>-0.014</td><td>-0.004</td><td>-0.023**</td><td>-0.03***</td><td>-0.02**</td><td>-0.035***</td><td>-0.063***</td></tr><tr><td rowspan="4">Credit Demand</td><td>BASE</td><td>0.011**</td><td>0.012**</td><td>0.012**</td><td>0.01**</td><td>0.007</td><td>0</td><td>0.008</td><td>0.004</td><td>0.001</td><td>0.006</td><td>-0.008</td></tr><tr><td>SEMI</td><td>0.009</td><td>0.005</td><td>-0.003</td><td>0.003</td><td>0.008</td><td>0.004</td><td>0.003</td><td>0.005</td><td>-0.004</td><td>0.003</td><td>-0.002</td></tr><tr><td>NEVA</td><td>0.002</td><td>0.005</td><td>0.001</td><td>0</td><td>-0.001</td><td>0.007</td><td>0.002</td><td>-0.001</td><td>0.001</td><td>-0.007</td><td>-0.009</td></tr><tr><td>NEVA<sup>2</sup></td><td>0</td><td>0.002</td><td>0.006</td><td>0.001</td><td>0.002</td><td>0.007</td><td>-0.001</td><td>-0.004</td><td>0.001</td><td>-0.007</td><td>-0.017***</td></tr><tr><td rowspan="4">Fin. constraints</td><td>BASE</td><td>-0.055***</td><td>-0.06***</td><td>-0.046***</td><td>-0.038**</td><td>-0.025</td><td>0</td><td>-0.007</td><td>-0.002</td><td>0.007</td><td>0.001</td><td>0.055***</td></tr><tr><td>SEMI</td><td>-0.044***</td><td>-0.027</td><td>-0.004</td><td>-0.021</td><td>-0.021</td><td>-0.012</td><td>0.008</td><td>0.013</td><td>0.025</td><td>0.022</td><td>0.051**</td></tr><tr><td>NEVA</td><td>-0.018</td><td>-0.007</td><td>0.003</td><td>0.003</td><td>0.029</td><td>-0.01</td><td>0.015</td><td>0.038*</td><td>0.043**</td><td>0.053**</td><td>0.073***</td></tr><tr><td>NEVA<sup>2</sup></td><td>-0.003</td><td>-0.01</td><td>-0.014</td><td>-0.003</td><td>0.011</td><td>-0.011</td><td>0.026</td><td>0.055***</td><td>0.034*</td><td>0.052**</td><td>0.124***</td></tr><tr><td rowspan="4">GDP growth</td><td>BASE</td><td>0.01**</td><td>0.016***</td><td>0.011***</td><td>0.007*</td><td>0.008*</td><td>0</td><td>0.003</td><td>0</td><td>0.002</td><td>0</td><td>-0.01*</td></tr><tr><td>SEMI</td><td>0.009**</td><td>0.007</td><td>0</td><td>0.005</td><td>0.005</td><td>0.002</td><td>-0.003</td><td>0.001</td><td>-0.006</td><td>-0.003</td><td>-0.008</td></tr><tr><td>NEVA</td><td>0.003</td><td>0.001</td><td>0.001</td><td>-0.001</td><td>-0.005</td><td>0.004</td><td>-0.001</td><td>-0.009*</td><td>-0.009*</td><td>-0.013***</td><td>-0.015***</td></tr><tr><td>NEVA<sup>2</sup></td><td>0.002</td><td>0.004</td><td>0.005</td><td>-0.001</td><td>-0.001</td><td>0.004</td><td>-0.004</td><td>-0.012**</td><td>-0.005</td><td>-0.014***</td><td>-0.024***</td></tr><tr><td rowspan="4">Investment growth</td><td>BASE</td><td>0.02**</td><td>0.034***</td><td>0.026***</td><td>0.016**</td><td>0.017**</td><td>0</td><td>0.006</td><td>0.004</td><td>0.005</td><td>0.001</td><td>-0.01</td></tr><tr><td>SEMI</td><td>0.019**</td><td>0.016**</td><td>0.01</td><td>0.013*</td><td>0.012</td><td>0.009</td><td>-0.006</td><td>0.003</td><td>-0.009</td><td>-0.002</td><td>-0.013</td></tr><tr><td>NEVA</td><td>0.006</td><td>0</td><td>0.003</td><td>-0.003</td><td>-0.013</td><td>0.003</td><td>-0.012</td><td>-0.015*</td><td>-0.015</td><td>-0.022**</td><td>-0.023**</td></tr><tr><td>NEVA<sup>2</sup></td><td>0.008</td><td>0.003</td><td>0.003</td><td>-0.006</td><td>-0.014</td><td>0.003</td><td>-0.01</td><td>-0.027***</td><td>-0.012</td><td>-0.033***</td><td>-0.048***</td></tr><tr><td rowspan="4">Unemp. rate</td><td>BASE</td><td>-0.218***</td><td>-0.232***</td><td>-0.195***</td><td>-0.158**</td><td>-0.092</td><td>0</td><td>-0.042</td><td>-0.026</td><td>0.008</td><td>-0.023</td><td>0.195**</td></tr><tr><td>SEMI</td><td>-0.171**</td><td>-0.08</td><td>0.005</td><td>-0.057</td><td>-0.088</td><td>-0.063</td><td>0.034</td><td>-0.046</td><td>0.098</td><td>0.05</td><td>0.158*</td></tr><tr><td>NEVA</td><td>-0.125*</td><td>-0.078</td><td>-0.044</td><td>-0.042</td><td>0.048</td><td>-0.108</td><td>-0.014</td><td>0.06</td><td>0.067</td><td>0.092</td><td>0.188**</td></tr><tr><td>NEVA<sup>2</sup></td><td>-0.054</td><td>-0.077</td><td>-0.109</td><td>-0.079</td><td>-0.023</td><td>-0.104</td><td>0.022</td><td>0.111</td><td>0.032</td><td>0.108</td><td>0.375***</td></tr></tbody></table>

Notes: The table shows, for a set of macroeconomic real and financial indicators, the Monte Carlo average percentage difference between the value of statistic X under the scenario of interest s and a baseline scenario base, $(X_s - X_{base})/X_{base}$. In order to have statistics comparable across different capital requirement regimes, a reference-baseline scenario is fixed at $\tau^B = 0.045$, i.e., the baseline parametrization in Section 3, centered around 0 by construction.

We also test whether the difference in means is statistically significant or not between the two scenarios. The null hypothesis is that there is no statistical difference in the means calculated in the two scenarios: "*p<0.1; **p<0.05; ***p<0.01"

<caption>Table 6: Macroeconomic effects of NEVA implementation under different capital requirement regimes.</caption>
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Fagiolo, G., M. Napoletano, and A. Roventini (2008). Are output growth-rate distributions fat-tailed? some evidence from oecd countries. *Journal of Applied Econometrics* 23(5), 639–669.

Fagiolo, G. and A. Roventini (2017). Macroeconomic policy in dsge and agent-based models redux: New developments and challenges ahead. *Journal of Artificial Societies and Social Simulation* 20(1), 1.

Farmer, J. D. and D. Foley (2009). The economy needs agent-based modelling. *Nature* 460(7256), 685–686.

Foos, D., L. Norden, and M. Weber (2010). Loan growth and riskiness of banks. *Journal of Banking & Finance* 34(12), 2929–2940.

Fricke, D. and T. Lux (2015, Mar). Core-periphery structure in the overnight money market: Evidence from the e-mid trading platform. *Computational Economics* 45(3), 359–395.

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Fagiolo, G., M. Guerini, F. Lamperti, A. Moneta, and A. Roventini (2019). Validation of agent-based models in economics and finance. In *Computer simulation validation*, pp. 763–787. Springer.

Fagiolo, G., M. Napoletano, and A. Roventini (2008). Are output growth-rate distributions fat-tailed? some evidence from oecd countries. *Journal of Applied Econometrics* 23(5), 639–669.

Fagiolo, G. and A. Roventini (2017). Macroeconomic policy in dsge and agent-based models redux: New developments and challenges ahead. *Journal of Artificial Societies and Social Simulation* 20(1), 1.

Farmer, J. D. and D. Foley (2009). The economy needs agent-based modelling. *Nature* 460(7256), 685–686.

Foos, D., L. Norden, and M. Weber (2010). Loan growth and riskiness of banks. *Journal of Banking & Finance* 34(12), 2929–2940.

Fricke, D. and T. Lux (2015, Mar). Core-periphery structure in the overnight money market: Evidence from the e-mid trading platform. *Computational Economics* 45(3), 359–395.

Gai, P., A. Haldane, and S. Kapadia (2011). Complexity, concentration and contagion. *Journal of Monetary Economics* 58(5), 453–470.

Galbiati, M. and K. Soramäki (2011). An agent-based model of payment systems. *Journal of Economic Dynamics and Control* 35(6), 859–875.

Georg, C.-P. (2013). The effect of the interbank network structure on contagion and common shocks. *Journal of Banking & Finance* 37(7), 2216–2228.

Gertler, M. and N. Kiyotaki (2010). Financial intermediation and credit policy in business cycle analysis. In *Handbook of monetary economics*, Volume 3, pp. 547–599. Elsevier.
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Gross, M., J. Henry, and W. Semmler (2018). Destabilizing effects of bank overleveraging on real activity—an analysis based on a threshold mcs-gvar. *Macroeconomic Dynamics* 22(7), 1750–1768.

Guerini, M., F. Lamperti, M. Napoletano, A. Roventini, and T. Treibich (2022, Apr). Unconventional monetary policies in an agent-based model with mark-to-market standards. *Review of Evolutionary Political Economy* 3(1), 73–107.

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Glasserman, P. and H. P. Young (2016). Contagion in financial networks. *Journal of Economic Literature* 54(3), 779–831.

Godley, W. and M. Lavoie (2006). *Monetary economics: an integrated approach to credit, money, income, production and wealth*. Springer.

Gross, M., J. Henry, and W. Semmler (2018). Destabilizing effects of bank overleveraging on real activity—an analysis based on a threshold mcs-gvar. *Macroeconomic Dynamics* 22(7), 1750–1768.

Guerini, M., F. Lamperti, M. Napoletano, A. Roventini, and T. Treibich (2022, Apr). Unconventional monetary policies in an agent-based model with mark-to-market standards. *Review of Evolutionary Political Economy* 3(1), 73–107.

Gurgone, A., G. Iori, and S. Jafarey (2018). The effects of interbank networks on efficiency and stability in a macroeconomic agent-based model. *Journal of Economic Dynamics and Control* 91, 257–288.

Haldane, A. (2012). The dog and the frisbee, speech given at the federal reserve bank of kansas city's 36th economic policy symposium "the changing policy landscape". Given at the Federal Reserve Bank of Kansas City's 36th economic policy symposium, "The Changing Policy Landscape", Jackson Hole, Wyoming.

Ihrig, J. E., G. C. Weinbach, S. A. Wolla, et al. (2021). Teaching the linkage between banks and the fed: RIP Money Multiplier. *Page One Economics Newsletter*. Federal Reserve Bank of St. Louis.

Jakab, Z. and M. Kumhof (2014). Models of banking: loanable funds or loans that create funds? Technical report, Available at SSRN 2474759.

Jakab, Z. and M. Kumhof (2018, Oct). Banks are not intermediaries of loanable funds — facts, theory and evidence. Bank of England working papers 761, Bank of England.

Kiyotaki, N. and J. Moore (1997). Credit cycles. *Journal of political economy* 105(2), 211–248.

Kiyotaki, N. and J. Moore (2002). Balance-sheet contagion. *American Economic Review* 92(2), 46–50.

Kuznets, S. and J. T. Murphy (1966). *Modern economic growth: Rate, structure, and spread*, Volume 2. Yale University Press New Haven.

Laeven, L. and F. Valencia (2012). Systemic banking crises database: An update. Technical report, IMF working paper 12/163.

Lamperti, F., V. Bosetti, A. Roventini, and M. Tavoni (2019). The public costs of climate-induced financial instability. *Nature Climate Change* 9(11), 829–833.

Lamperti, F., V. Bosetti, A. Roventini, M. Tavoni, and T. Treibich (2021). Three green financial policies to address climate risks. *Journal of Financial Stability* 54, 100875.

Lamperti, F., G. Dosi, M. Napoletano, A. Roventini, and A. Sapio (2018). Faraway, so close: coupled climate and economic dynamics in an agent-based integrated assessment model. *Ecological Economics* 150, 315–339.
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Lown, C. and D. P. Morgan (2006). The credit cycle and the business cycle: New findings using the loan officer opinion survey. *Journal of Money, Credit and Banking* 38(6), 1575–1597.

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Luu, D. T., M. Napoletano, P. Barucca, and S. Battiston (2021). Collateral unchained: Rehypothecation networks, concentration and systemic effects. *Journal of Financial Stability* 52, 100811.

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Napoletano, M., A. Roventini, and S. Sapio (2006). Are business cycles all alike? a bandpass filter analysis of the italian and us cycles. *Rivista italiana degli economisti* 11(1), 87–118.

Newman, M. (2018). *Networks*. Oxford university press.

Newman, M. E. (2003). The structure and function of complex networks. *SIAM review* 45(2), 167–256.

Osinski, J., K. Seal, and M. L. Hoogduin (2013). *Macroprudential and microprudential policies: toward cohabitation*. International Monetary Fund.

Palley, T. I. (1996). Growth theory in a keynesian mode: Some keynesian foundations for new endogenous growth theory. *Journal of Post Keynesian Economics* 19(1), 113–135.

Popoyan, L., M. Napoletano, and A. Roventini (2017). Taming macroeconomic instability: Monetary and macro-prudential policy interactions in an agent-based model. *Journal of Economic Behavior & Organization* 134, 117–140.

Popoyan, L., M. Napoletano, and A. Roventini (2020). Winter is possibly not coming: Mitigating financial instability in an agent-based model with interbank market. *Journal of Economic Dynamics and Control* 117, 103937.
Lavoie, M. (2014). *Post-Keynesian economics: new foundations*. Edward Elgar Publishing.

Leary, M. T. (2009). Bank loan supply, lender choice, and corporate capital structure. *The Journal of Finance* 64(3), 1143–1185.

Liu, A., M. Paddrik, S. Y. Yang, and X. Zhang (2020). Interbank contagion: An agent-based model approach to endogenously formed networks. *Journal of Banking & Finance* 112, 105191.

Lown, C. and D. P. Morgan (2006). The credit cycle and the business cycle: New findings using the loan officer opinion survey. *Journal of Money, Credit and Banking* 38(6), 1575–1597.

Luu, D. T. and T. Lux (2019). Multilayer overlaps and correlations in the bank-firm credit network of spain. *Quantitative Finance* 19(12), 1–22.

Luu, D. T., M. Napoletano, P. Barucca, and S. Battiston (2021). Collateral unchained: Rehypothecation networks, concentration and systemic effects. *Journal of Financial Stability* 52, 100811.

Lux, T. (2015). Emergence of a core-periphery structure in a simple dynamic model of the interbank market. *Journal of Economic Dynamics and Control* 52, A11–A23.

McLeay, M., A. Radia, and R. Thomas (2014). Money creation in the modern economy. *Bank of England Quarterly Bulletin* Q1, 14–27.

Montagna, M. and C. Kok (2016). Multi-layered interbank model for assessing systemic risk. Technical Report 1944, ECB Working Paper.

Müller, J. (2006). Interbank credit lines as a channel of contagion. *Journal of Financial Services Research* 29(1), 37–60.

Napoletano, M., A. Roventini, and S. Sapio (2006). Are business cycles all alike? a bandpass filter analysis of the italian and us cycles. *Rivista italiana degli economisti* 11(1), 87–118.

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Newman, M. E. (2003). The structure and function of complex networks. *SIAM review* 45(2), 167–256.

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Tedeschi, G., A. Mazloumian, M. Gallegati, and D. Helbing (2012). Bankruptcy cascades in interbank markets. *PloS one* 7(12), e52749.

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Windrum, P., G. Fagiolo, and A. Moneta (2007). Empirical validation of agent-based models: Alternatives and prospects. *Journal of Artificial Societies and Social Simulation* 10(2), 8.

Wright, I. (2005). The duration of recessions follows an exponential not a power law. *Physica A: Statistical Mechanics and its Applications* 345(3-4), 608–610.

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## ABOUT OFCE

The Paris-based Observatoire français des conjonctures économiques (OFCE), or French Economic Observatory is an independent and publicly-funded centre whose activities focus on economic research, forecasting and the evaluation of public policy.

Its 1981 founding charter established it as part of the French Fondation nationale des sciences politiques (Sciences Po), and gave it the mission to "ensure that the fruits of scientific rigour and academic independence serve the public debate about the economy". The OFCE fulfils this mission by conducting theoretical and empirical studies, taking part in international scientific networks, and assuring a regular presence in the media through close cooperation with the French and European public authorities. The work of the OFCE covers most fields of economic analysis, from macroeconomics, growth, social welfare programmes, taxation and employment policy to sustainable development, competition, innovation and regulatory affairs.

## ABOUT SCIENCES PO

Sciences Po is an institution of higher education and research in the humanities and social sciences. Its work in law, economics, history, political science and sociology is pursued through ten research units and several crosscutting programmes.

Its research community includes over two hundred twenty members and three hundred fifty PhD candidates. Recognized internationally, their work covers a wide range of topics including education, democracies, urban development, globalization and public health.

One of Sciences Po's key objectives is to make a significant contribution to methodological, epistemological and theoretical advances in the humanities and social sciences. Sciences Po's mission is also to share the results of its research with the international research community, students, and more broadly, society as a whole.

## PARTNERSHIP
## ABOUT OFCE

The Paris-based Observatoire français des conjonctures économiques (OFCE), or French Economic Observatory is an independent and publicly-funded centre whose activities focus on economic research, forecasting and the evaluation of public policy.

Its 1981 founding charter established it as part of the French Fondation nationale des sciences politiques (Sciences Po), and gave it the mission to "ensure that the fruits of scientific rigour and academic independence serve the public debate about the economy". The OFCE fulfils this mission by conducting theoretical and empirical studies, taking part in international scientific networks, and assuring a regular presence in the media through close cooperation with the French and European public authorities. The work of the OFCE covers most fields of economic analysis, from macroeconomics, growth, social welfare programmes, taxation and employment policy to sustainable development, competition, innovation and regulatory affairs.

## ABOUT SCIENCES PO

Sciences Po is an institution of higher education and research in the humanities and social sciences. Its work in law, economics, history, political science and sociology is pursued through ten research units and several crosscutting programmes.

Its research community includes over two hundred twenty members and three hundred fifty PhD candidates. Recognized internationally, their work covers a wide range of topics including education, democracies, urban development, globalization and public health.

One of Sciences Po's key objectives is to make a significant contribution to methodological, epistemological and theoretical advances in the humanities and social sciences. Sciences Po's mission is also to share the results of its research with the international research community, students, and more broadly, society as a whole.

## PARTNERSHIP