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From financial instability to green finance: the role of banking and credit market regulation in the Eurace model

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Abstract

We investigate appropriate banking and regulatory policies aimed at pushing the banking sector to shift from speculative lending, the cause of asset bubbles and economic crises, to green investments lending, so as to foster the transition to a more energy efficient production technology. For this purpose, we consider an enriched version of the Eurace model, which includes heterogenous capital goods, allowing for different degrees of energy efficiency in the production technology. Credit money in Eurace is endogenous and limited by Basel capital adequacy regulation on the supply side, while on the demand side it is determined by firms’ investments and households’ house purchasing. We introduce a differentiation of capital requirements according to the destination of lending, demanding higher bank capital in the case of speculative lending, thus encouraging banks to finance firm investment. As up-to-date capital goods have better energy efficiency in the model design, a higher pace of investment implies also a positive environmental effect. Results suggest that the proposed regulation is able to foster investments and capital accumulation in the short term, improving the energy efficiency of firms. However, reducing mortgages with a restrictive regulation has a negative impact on total private credit, and thus on endogenous money supply, weakening consumption and aggregate demand. In the long term, the contraction of total credit becomes stronger, and the negative outcomes on aggregate demand also affect investment. Therefore, in the long run, the positive effects on capital and energy efficiency become negligible, while the main economic indicators deteriorate.

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Notes

  1. See, e.g., the repeal of the carbon tax by the new Australian government in 2014 or the debate in the US 2016 presidential race

  2. A non-exhaustive list could include the K+S model. See Dosi et al. (2010, 2013, 2015), the set of models developed by the Ancona research group (Caiani et al. 2016; Riccetti et al. 2015; Russo et al. 2016), the CC-MABM (Assenza et al. 2015), the Mark I CRISIS model (Klimek et al. 2015; Gualdi et al. 2015), Iceace (Erlingsson et al. 2014), Eurace (Cincotti et al. 2012a) and Eurace@UNIBI (Dawid et al. 2016)

  3. It is worth noting the relevance of energy efficiency in the EU environmental policy framework where a 20% increase in energy efficiency by 2020 with respect to 1990 is among the three well-known 20-20-20 targets set by the European Union in 2009; see: http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32009D0406

  4. FP6 European Project EURACE and EU-FP7 project SYMPHONY

  5. In the paper, we will use the terms electricity and energy interchangeably, with no distinction.

  6. The power producer (PP) agent is a very stylized agent that imports fossil fuels from the foreign sector at price pO and produces electricity on request with no labor force needed. PP profits are given by the aggregate amount of energy consumed by the production sector, multiplied by difference between between pE and pO. PP profits are paid out to shareholders (households) in the Eurace economy.

  7. This assumption is supported by empirical evidence. In particular, the latest Energy Efficiency Market Report by the International Energy Agency points out that global energy intensity improved by 1.8% in 2015 and by 1.5% in 2014, while the average yearly improvement was around 0.6% in the decade between 2003 and 2013 (IEA 2016).

  8. It is worth noting that the additional output is assumed to be a decreasing function of m to take into account the investments depreciation; see Teglio et al. (2017, Appendix, Eq. 5).

  9. Δ𝜖f shall be considered in absolute terms.

  10. This could be considered what is usually known as Minsky moment; see Minsky (1986).

  11. https://www.eia.gov/outlooks/aeo/pdf/appa.pdf

  12. http://pubdocs.worldbank.org/en/678421508960789762/CMO-October-2017-Forecasts.pdf

  13. https://www.imf.org/en/Publications/WEO/Issues/2017/09/19/~/media/Files/Publications/WEO/2017/October/pdf/main-chapter/tblparta.ashx

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Acknowledgments

The authors acknowledge EU-FP7 collaborative project SYMPHONY under grant no. 611875.

Author information

Authors and Affiliations

  1. DIME, University of Genoa, Genoa, Italy

    Marco Raberto, Linda Ponta & Silvano Cincotti

  2. Department of Economics, University Jaume I, Castellon, Spain

    Bulent Ozel & Andrea Teglio

  3. School of Engineering, LIUC - Cattaneo University, Castellanza, Italy

    Linda Ponta

  4. Department of Economics, Ca’ Foscari University of Venice, Venice, Italy

    Andrea Teglio

Authors
  1. Marco Raberto
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  2. Bulent Ozel
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  4. Andrea Teglio
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Corresponding author

Correspondence to Marco Raberto.

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This study was funded by EU-FP7 (grant number 611875).

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Appendix:

Appendix:

1.1 A1 - The housing market

The housing market is active every first day of the month. Households play the role of buyers and sellers in the market and can sell or buy one housing unit at a time; housing units are homogeneous. The market is characterized by decentralized exchange and posted prices set by sellers.

Household decision making about house purchase and sale is mainly subject to random behavior in order to give more relevance to the credit implications and their impact on the economy as a whole, rather than on the behavioral aspects of the housing market. In particular, the parameter Φ sets the probability for each household to be active in the housing market the first day of each month, unless the household is financially distressed, i.e. he is facing mortgage payments (interests + principal) higher than a given fraction 𝜃fs of his income (labor + capital), where both mortgage payments and income refer to the last quarter. If randomly selected to be active, the household can assume the role of buyer or seller with equal likelihood. By contrast, if financially distressed, say the fire sale case, we stipulate that the household enters the market to sell one housing unit at a discounted price with respect to the last average market price, so as to increase the likelihood that a transaction takes place, in order to reduce the mortgage burden as well as the debt service. Furthermore, in the case the ratio between quarterly mortgage payments and quarterly income is higher than the threshold 𝜃d, where 𝜃d > 𝜃fs, then the household defaults on his mortgages, which are partially written-off with a consequent loss on the balance sheet of the lending bank.

If a household is randomly selected to enter the housing market with a seller role, then he posts one of his housing units for sale at higher price than the previous month average market price. In particular, the selling price is higher by a percentage value, which is a random draw uniformly distributed between 0 and ψup. This model feature can be justified based on the assumption that households randomly selected for the seller role do not have any particular necessity to liquidate their housing units. Therefore, we make the reasonable assumption that they are considering the sale of a housing unit only if they can realize a small random gain with respect to the latest average housing market price. Conversely, if a household enters the market with a seller role because financially distressed (fire sale case), then to facilitate liquidation, we assume he posts one of his housing units for sale at a lower price than the previous average market price by a percentage value that is a random draw uniformly distributed between 0 and ψdown.

Households that have been randomly selected as buyers are randomly queued and in turn select to purchase the cheapest available housing unit. A transaction takes place at the posted sale price if the household is able to get a mortgage from a bank to cover the entire value of the house. Therefore, for the sake of simplicity, we assume that all granted mortgages are characterized by a loan-to-value (LTV) ratio equal to one, and so households do not use their liquidity when buying a housing unit but just money borrowed from a bank. This modeling feature has been chosen in order to avoid a direct and simultaneous interaction of the housing market purchasing behavior with the saving and investing decision in the financial market. In the case a transaction takes place, the selling agent repays back to the bank the mortgage associated with the sold housing unit. The housing market session closes when all buyers had their turn or there are no more houses for sale. A new housing price is then computed as the average of realized transaction prices.

Banks can provide variable-rate mortgages to households; the annualized mortgage rate is determined on a monthly basis as a mark-up on the rate set by the central bank. Households are due to reimburse the mortgage over a period of 30 years through monthly mortgage payments which include both the interests and the principal installment. Principal installments for each mortgage are constant over the repaying period and are computed as a ratio between the initial mortgage amount and 360, i.e. the mortgage duration in months. Monthly interest payments are determined by the outstanding mortgage principal and the annualized mortgage rate divided by 12, i.e., the number of months in a year. Banks, whenever they receive a mortgage request by a household, assess his capability to afford mortgage repayments by comparing household’s net income (both labor and capital) earned in the last quarter with household’s expected quarterly mortgage payments, including both old outstanding mortgages and the new requested mortgage. Banks grant the requested mortgage provided the capital requirement rule is fulfilled and the ratio between expected quarterly mortgage payments of the household and his latest net quarterly income is lower or equal than a pre-determined threshold, which is called debt-service-to-income (DSTI) ratio.

1.2 A2 - Stocks and flows accounting

This section provides a compact description of the model according to the “stock-flow consistent” approach along the lines introduced by Godley and Lavoie (2012). This approach allows us to check the consistency between stocks and flows in the model, both at the level of the single agent and at the aggregate one, in line also with post-Keynesian stock-flow-consistent modeling approach; see also Caverzasi and Godin (2015).

A detailed description of the behavioral rules characterizing each agent is reported in Teglio et al. (2017), whereas the details of the housing market mechanism are illustrated in Ozel et al. (2016).

Here, we present three matrices that show:

  • Agent class balance sheets

  • Sectorial balance sheet

  • Stock and monetary flows among sectors.

In particular, Table 2 reports the balance sheets of each agent class that populates the Eurace economy. Table 3 shows all assets and liabilities for each sector (here a sector is the aggregate set of agents belonging to the same class). Finally, Table 4, called transaction flow matrix, shows all the stock and monetary flows among sectors.

1.3 A3 - Agent class balance sheets

The balance sheets of any class of agents populating the Eurace economy is shown in Table 2. Each agent is characterized by liquidity M in the assets side and by equity E in the liabilities side. Households are characterized by a portfolio of stock shares and government bonds and by housing units in the assets side and by mortgages in the liabilities side. Capital goods and inventories, in the assets side, and debt, in the liabilities side, characterize the consumption goods producer class. The assets side of the bank agent is defined by loans and mortgages whereas the liabilities side by deposits and debt. Issued bonds are a liability for the government. Finally, the Central Bank is characterized by loans and government bonds in the assets side and deposits and fiat money in the liabilities side.

Table 2 Agent class balance sheets
Full size table

1.4 A4 - Sectorial balance sheets

Table 3 shows, in a compact way, the relation among sectors. In details, a plus (minus) sign corresponds to agents’ assets (liabilities) and each column can be read as the aggregated balance sheet of a specific sector. Rows show liabilities and the corresponding claims (assets) among sectors, thus generally adding up to zero. Exceptions are capital and inventories, accumulated by CGPs, housing units belonging to households and households’ equity shares, which are issued by CGPs, KGP, power producer and banks and do not add up to zero because of the difference between market price and book value of equity.

Table 3 Sectorial balance sheet matrix
Full size table

Finally, it is worth noting that, in Table 3, the difference between fiat money (on the liability side) and central bank liquidity (on the asset side) is always constant (and equal to the initial central bank liquidity MCB,0). Fiat money is the money created by the central bank to provide loans to commercial banks when they are in liquidity shortage, or to buy government bonds in the secondary market, through quantitative easing operations. Households that sell government bonds to the central bank deposit the sale proceeds at their own banks, while the money lent to banks by the central bank is lent to households of firms, then in turn deposited again in the banking sector. Therefore, in both cases, the liquidity of the banking sector is increased by an amount equal to the new fiat money created and this additional liquidity is deposited by banks at the central bank, increasing central bank liquidity by an amount equal to the Fiat money originally created. It is worth noting, however, that the money supply in the economy can vary independently from the fiat money created by the central bank, because it endogenously raises every time a bank grants a new loan or mortgage and it decreases when the loan or mortgage is paid back.

1.5 A5 - Stock and monetary flows among sectors

All the stock and monetary flows among agents are described in the transaction flow matrix (Table 4), where the current account describes the flows of revenues (plus sign) and payments (minus sign) that agents get and make. Rows show the monetary flows among agents. The result of agents’ transactions is the net cash flow.

The capital account section of Table 4 describes the balance sheet changes related to each sector.

Table 4 Sectorial transaction flow matrix of agents populating the EURACE economy
Full size table

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Raberto, M., Ozel, B., Ponta, L. et al. From financial instability to green finance: the role of banking and credit market regulation in the Eurace model. J Evol Econ 29, 429–465 (2019). https://doi.org/10.1007/s00191-018-0568-2

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