The impacts of credit standards on aggregate fluctuations in a small open economy: The role of monetary policy

Le, Hai Article The impacts of credit standards on aggregate fluctuations in a small open economy: The role of monetary policy Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Le, Hai (2021) : The impacts of credit standards on aggregate fluctuations in a small open economy: The role of monetary policy, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 9, Iss. 4, pp. 1-26, https://doi.org/10.3390/economies9040203 This Version is available at: https://hdl.handle.net/10419/257361 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ economies Article The Impacts of Credit Standards on Aggregate Fluctuations in a Small Open Economy: The Role of Monetary Policy Hai Le 1,2   Citation: Le, Hai. 2021. The Impacts of Credit Standards on Aggregate Fluctuations in a Small Open Economy: The Role of Monetary Policy. Economies 9: 203. https:// doi.org/10.3390/economies9040203 Academic Editor: Helmi Hamdi Received: 29 October 2021 Accepted: 14 December 2021 Published: 20 December 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Graduate School of Economics, Kyoto University, Kyoto 606-8501, Japan; r[email protected] 2Faculty of International Business, Banking Academy, Hanoi 100000, Vietnam Abstract: Empirical evidence demonstrates that credit standards, including lending margins and collateral requirements, move in a countercyclical direction. In this study, we construct a small open economy model with financial frictions to generate the countercyclical movement in credit standards. Our analysis demonstrates that countercyclical fluctuations in credit standards work as an amplifier of shocks to the economy. In particular, the existence of endogenous credit standards increases output volatility by 21%. We also suggest three alternative tools for policymakers to dampen the effects of endogenous credit standards on macroeconomic volatility. First, the introduction of credit growth to the monetary policy succeeds in counteracting the fluctuation of lending, and thus decreasing the additional volatility considerably. Second, the exchange rate augmented monetary policy, if well-constructed, is considered an efficient tool to eliminate most of the additional fluctuations caused by deep habits in the banking sector. Finally, the introduction of the foreign interest augmented policy also proves successful in dampening the effect of endogenous movements in lending standards. Keywords: credit standards; deep habits; monetary policy; DSGE modeling; small open economy; aggregate fluctuations; collateral requirements JEL Classification: E12; E22; E23; E31; E32; E44; F34; F41 1. Introduction Credit standards, such as banking spreads and collateral requirements, move in a countercyclical direction, according to numerous empirical investigations. The Ref. (Santos and Winton 2008), employing US data for the credit market, showed that banking markup can rise up to 95 basis points in a recession. Even when credit risk is taken into consideration, the Ref. (Aliaga-Díaz and Olivero 2011) demonstrated that countercyclical banking spreads can be found. Similar results were observed in numerous OECD countries using both Bankscope data and International Financial Statistics (IFS) data (see (Olivero 2010)). As for the empirical evidence supporting countercyclical fluctuations in collateral requirements, the Ref. (Asea and Blomberg 1998), using a large dataset for commercial and industrial loans issued in the US during the period 1977–1993, indicated that a remarkable increase in the probability of collateral pledge is attributed to higher aggregate unemployment. In other words, collateral requirements are empirically proven to be countercyclical. Similarly, the Ref. (Jimenez et al. 2006) employed data from Spain for all loans exceeding 6000 euros made between 1984 and 2002 to show that loans made during booms are less likely to be collateralized than those made during downturns. First, deep habits in banking have been shown to be effective at capturing characteristics of the lending relationship between borrowers and lenders. 1 Therefore, we follow (Aliaga-Díaz and Olivero 2010) and assume that wholesale good entrepreneurs form deep habits in the demand for loans from banks to incorporate the lending relationship in our model. Second, empirical evidence has indicated that collateral requirements, as one measure of credit standards, fluctuate over the business cycle and that they move in a countercyclical fashion. To account for this finding, we follow (Ravn 2016) and endogenize the Economies 2021,9, 203. https://doi.org/10.3390/economies9040203 https://www.mdpi.com/journal/economies Economies 2021,9, 203 2 of 26 fluctuation in collateral requirements into our model by an assumption that banks compete with each other in both the interest rate spread and collateral pledge when giving loans. 2 Finally, we extend the current setting to a small open economy model by introducing the small open economy feature of (Galí and Monacelli 2016). Specifically, we assume that the size of the domestic economy is relatively small compared to that of the world economy. As a result, we can neglect its impact on the world economy, and thus consider the world aggregate as exogenous. However, unlike (Galí and Monacelli 2016) who assumed the existence of complete international financial markets to close the open economy, we relax this assumption and allow for the incomplete asset markets. To induce stationarity, we follow (Schmitt-Grohé and Uribe 2003) and employ the debt elastic interest rate to close our model. The paper is motivated by the following questions which were not addressed in the previous deep habits-related literature: How do fluctuations in credit standards emerging from deep habits in the banking sector amplify macroeconomic volatility in a small open economy setting? In particular, what are the quantitative impacts of endogenous credit standards on output volatility when taking into account the open economy features? How do credit standards move in response to an increase in foreign demand? What are the recommendations for policymakers in order to diminish the volatility brought about by the existence of the lending relationship? To answer these questions, we incorporate four shocks into our model: (1) technology shock; (2) labor supply shock; (3) monetary policy shock; and (4) foreign demand shock, as in (Gali and Monacelli 2005), and calibrate the model based on Swedish data. 3 We find that the countercyclical movement in credit standards indeed works as a financial accelerator of these shocks to the economy. More specifically, the presence of credit standards increases output volatility by approximately 21%. This demonstrates the quantitative importance of endogenous credit standards over the business cycle that we should take into account in our analysis. To combat the impact of endogenous fluctuations in credit standards, we introduce three alternative monetary policies. First, we show that a credit growth-augmented monetary policy is an effective tool in reducing the additional volatility arising from endogenous credit standards. Second, the addition of the exchange rate to the monetary policy also proves to be successful in counteracting the fluctuations in lending, thus eliminating most of the additional volatility. Third, we let the policy interest rate respond to changes in the foreign interest rate in order to indirectly counteract movements in lending. This policy, if well-designed, can substantially eliminate the additional volatility. There have been other studies investigating the impacts of deep habits in the banking sector on economic fluctuation. However, to the best of our knowledge, existing studies on this field all employ the closed-economy framework. The Ref. (Aliaga-Díaz and Olivero 2010), using the deep habits mechanism developed by (Ravn et al. 2006) for the banking sector to model the switching cost of borrowers, showed that the interest rate spread moves in a countercyclical pattern, as observed in the US data. Furthermore, they found that countercyclical spreads do indeed work as a financial accelerator of the productivity shock in the US economy. The Ref. (Melina and Villa 2014), by endogenizing the bank spread through the deep habits framework in banking, was able to replicate the negative response of the spread to an expansionary fiscal shock observed in the data. Additionally, their findings point out that countercyclical fluctuations in the interest rate spread generate an amplification mechanism in the transmission of the government-spending shock. The Ref. (Ravn 2016) incorporated empirically demonstrated endogenous fluctuations in interest rate spreads and collateral requirements into macroeconomic models. It was shown that the countercyclical lending standards amplify the impacts of macroeconomic shocks on the economy, with output volatility going up by 25%. The Ref. (Melina and Villa 2018) incorporated the financial frictions arising from deep habits into the DSGE model and applied the Bayesian technique to estimate the model. They discovered that monetary policy in the United States responds to credit growth during the Great Moderation. The Ref. (Airaudo and Olivero 2019) used a DSGE model with financial frictions arising from the Economies 2021,9, 203 3 of 26 existence of the lending relationship in banking to examine the optimal monetary policy. Their analysis demonstrates that countercyclical fluctuations in lending spreads exacerbate the inflation-output trade-off when designing the optimal policy in both discretion and commitment instances. Furthermore, they showed that the welfare cost of committing to suboptimal rules increases as we raise the magnitude of deep habits. The Ref. (Shapiro and Olivero 2020) introduced deep habits into an RBC model with endogenous labor force participation to investigate the role of labor force participation as an accelerator of financial shocks in the model. They showed that the impacts of countercyclical spreads on labor market dynamics are magnified by endogenous participation. We also contribute to the growing body of literature on open economy models commenced by (Mendoza 1991). The Ref. (Monacelli 2005) introduced the imperfect exchange rate pass-through in the small open economy setting and found that the monetary policy analysis in an open economy model is not isomorphic to that in a closed version under the presence of incomplete pass-through. 4 The Ref. (Galí and Monacelli 2016) employed the small open economy model with sticky prices and sticky wages to investigate the impacts of increased wage flexibility. They discovered that higher wage flexibility leads to a reduction in welfare, especially in countries with a fixed exchange rate regime. Recently, many studies have incorporated financial frictions into the small open framework. The Ref. (Céspedes et al. 2004) introduced financial frictions formulated by (Bernanke et al. 1999) to investigate the relationships among balance sheets, exchange rates, and outcomes in the small open economy setting. They pointed out that the external financing premium determined by an entrepreneur’s net worth is unaffected by the exchange rate regime, which is contrary to the previous literature. In their model, they showed that the flexible exchange rate regime plays a better role in containing the external shocks and is optimal in terms of welfare. The Ref. (Christiano et al. 2011) incorporated both financial frictions and employment frictions into the small open economy framework, and employed Bayesian methods to estimate the model for Swedish data. They found that the entrepreneur’s wealth shock is crucial to explaining movements in both GDP and investment, whereas a shock to the marginal efficiency of investment only plays a limited role in variance decomposition. Their analysis also showed that, in general, the impact of demand shocks is reduced, while that of supply shocks is magnified once the open economy feature is introduced. The Ref. (Afrin 2020) investigated the impacts of financial frictions emerging from oligopolistic bank competition on Australian business cycles, and demonstrated that the oligopolistic banking sector produces a distinct shock propagation mechanism that frequently accelerates business cycles. The remainder of the paper is organized as follows. Section 2presents the small open economy model. Section 3shows the calibration strategies. We report the main results, robustness checks, and policy analyses in Section 4. Finally, Section 5concludes. 2. Model The DSGE model presented here is fully based on the models of (Ravn 2016); (Galí and Monacelli 2016); (Monacelli 2005); and (Schmitt-Grohé and Uribe 2003). The economy is inhabited by: (1) households; (2) entrepreneurs; (3) domestic retailers; (4) importers; (5) commercial banks; and (6) monetary authorities. Households choose consumption, deposit, and labor to maximize their utility subject to the budget constraint. Entrepreneurs, borrowing from banks, employ capital stock and labor services to produce homogeneous goods. Domestic retailers then differentiate the goods at no cost and resell them in a monopolistically competitive market for consumption, investment, and export. Importers also operate in monopolistic competition, importing differentiated goods from the world economy and selling them in the home economy. Banks maximize the expected discounted value of profits by choosing their demand for deposits, external debt, and loan rates. Economies 2021,9, 203 4 of 26 2.1. Households There is a continuum of households indexed by i∈( 0, 1 ) . Household preferences are given by the following utility function: E0 ∞ ∑ t=0 (βp)t"log(Ci,p t−hpCi,p t−1)−Zt (Ni t)1+ψ 1+ψ#, (1) where βp∈( 0, 1 ) , hp∈( 0, 1 ) , and ψ denote the discount factor, the habit parameter in consumption, and the inverse Frisch elasticity, respectively. Ci,p t is a composite consumption index, and Ni t is labor. The superscript p is used, since households are assumed to be more patient than entrepreneurs. Zt represents the disutility of a labor supply shock. The shock evolves as follows: log Zt=ρzlog Zt−1+ (1−ρz)log Z+σzεz,t, where εz,t is an i.i.d. process with standard deviation σz , Z> 0 is the steady-state value of the labor supply shock, and ρz∈( 0, 1 ) is the persistence of the shock. Let Ci,p t= [( 1 − v)1 η(Ci,p H,t)η−1 η+v1 η(Ci,p F,t)η−1 η] η η−1 be a composite index of domestic final good consumption Ci,p H,t produced by domestic retailers and imported good consumption Ci,p F,t imported by local retailers. η> 0 measures the intratemporal elasticity of substitution between domestic and imported goods. v∈( 0, 1 ) denotes the share of imported goods in the consumption basket of the home country. In each period t , households face two optimization problems: an optimal allocation of goods, and a utility maximization problem. First, the optimal allocation of expenditures between domestic and imported goods implies: Ci,p H,t= (1−v)PH,t Pt−η Ci,p t,Ci,p F,t=vPF,t Pt−η Ci,p t, (2) where Pt= [( 1 −v)P1−η H,t+vP1−η F,t]1 1−η denotes the consumer price index (CPI). PH,t= (R1 0P1−e Hj,tdj)1 1−e and PF,t= (R1 0P1−e Fj,tdj)1 1−e are the price indexes of domestic and imported final goods, respectively, both expressed in the home currency. e> 1 denotes the elasticity of substitution across the final goods within each category of domestic or foreign goods.5 Second, households, taking the deposit rate, nominal wage, and the sum of profit as given, choose the consumption, labor supply, and stock of deposit to maximize their utility function. The optimization can be summarized as follows: max Ci,p t,Ni t,Mi b,t E0 ∞ ∑ t=0 (βp)t"log(Ci,p t−hpCi,p t−1)−Zt (Ni t)1+ψ 1+ψ#, (3) s.t. PtCi,p t+Z1 0Mi b,tdb ≤WtNi t+Rd t−1Z1 0Mi b,t−1db +Υi t, (4) where Wt denotes the nominal wage, Rd t−1 is the gross interest rate on the deposit Mi b,t−1 of household i in bank b, and Υi t denotes the sum of profits gained by household i . Equation (4) represents the budget constraint of the household. The first-order conditions yield:6 1 Cp t−hpCp t−1 −βpEthp Cp t+1−hpCp t =λp t, (5) ZtNψ t=wtλp t, (6) Economies 2021,9, 203 5 of 26 λp t=βpRd tEt λp t+1 Πt+1!, (7) where λp t is the Lagrange multiplier associated with Equation (4), wt=Wt Pt is the real wage, and Πt+1=Pt+1 Pt is the gross inflation rate. Equation (6) describes the optimal choice for labor supply, while a combination of Equations (5) and (7) can be interpreted as an Euler equation for consumption. 2.2. Wholesale Good Entrepreneurs The economy is inhabited by a continuum of entrepreneurs indexed by e∈( 0, 1 ) . Entrepreneur e eventually maximizes the following utility acquired from consuming both domestic and imported final goods: E0 ∞ ∑ t=0 (βI)tlog(Ce,I t−hICe,I t−1), (8) where βI∈( 0, 1 ) and hI∈( 0, 1 ) denote the discount factor and the habit parameter in consumption, respectively. Similar to household consumption, it is assumed that the consumption of entrepreneur Ce,I t , defined as Ce,I t= [( 1 −v)1 η(Ce,I H,t)η−1 η+v1 η(Ce,I F,t)η−1 η] η η−1 , is a composite index of domestic and imported final goods.7 Following (Kiyotaki and Moore 1997); (Iacoviello 2005); (Gerali et al. 2010); and (Ravn 2016), we assume that the entrepreneur’s loan from each bank is restricted by the collateral constraint as follows: Le b,t≤1 Rl b,t ξb,tae t, (9) where Le b,t,Rl b,t, and ξb,tdenote the borrowing of entrepreneur efrom bank b, the bank b’s gross lending rate, and the loan to value (LTV) ratio allowed by bank b , respectively. 8ae t is the expected value of the entrepreneur e’s asset and is given as follows: ae t=EtQt+1Ke t, (10) where Qtdenotes the price of installed capital, and Ke tis the stock of capital of entrepreneur e. In each period t , entrepreneur e faces two main optimization problems: an optimal allocation of loans from different banks, which results in the lending relationship; and a utility maximization problem. The former can be summarized as follows: min Le b,tZ1 0 Γb,tLe b,tdb, (11) s.t. Le b,t≤1 Rl b,t ξb,tae t, (12) Z1 0(Le b,t−hlSl b,t−1) ηl−1 ηldb ηl ηl−1= (Dl t)e, (13) Sl b,t=ρlSl b,t−1+ (1−ρl)Lb,t, (14) where Le bt denotes the entrepreneur e ’s demand for loans offered by bank b , while (Dl t)e is the demand for loans by the firm augmented by lending relationships. The term Sl b,t , defined as Sl b,t=R1 0(Sl b,t)ede , indicates that habits are external, as in (Ravn et al. 2006) . The parameter hl∈( 0, 1 ) denotes the degree of habits in lending, ηl is the elasticity of substitu- Economies 2021,9, 203 6 of 26 tion across different banks’ loans, and ρl is the persistence of lending relationships. Γb,t is defined as Γb,t=Rl b,t+vb ξb,t , with the first term indicating the interest rate payments and the second one being the amount of collateral. The parameter vb denotes the relative weight of collateral-minimization desire. Equation (11) demonstrates the minimization problem of the entrepreneur. Following (Ravn 2016), we assume that entrepreneurs take both interest rate expenditures and collateral requirements into consideration when they choose optimal demand for loans from each bank. 9 Equation (13) shows that entrepreneurs form deep habits in their relationship with banks, while Equation (14) indicates the evolution of stock of habit. The assumption of deep habits in wholesale good entrepreneurs’ demand for bank loans, as explained by (Aliaga-Díaz and Olivero 2010), yields a wedge between effective borrowing and actual borrowing. The wedge displays switching costs. 10 Given a total demand for loan (Dl t)e , each entrepreneur e chooses Le b,t to minimize both the interest rate expenditure and the amount of collateral. The solution to the problem yields the demand for bank b’s loans: Le b,t=Γb,t Γt−ηl (Dl t)e+hlSl b,t−1, (15) where Γt≡Rl t+vb ξt , with Rl t≡hR1 0(Rl b,t)1−ηldbi1 1−ηl and ξt≡hR1 0ξ1−ηl b,ti1 1−ηl being the aggregate lending rate and LTV ratio, respectively. In addition to the allocation of lending expenditure, in each period t , entrepreneur e chooses consumption, capital, labor, investment, and borrowing to maximize the utility function. The maximization problem can be summarized as follows: max Ce,I t,Ke t,Ne t,Ie t,(Dl t)e E0 ∞ ∑ t=0 (βI)tlog(Ce,I t−hICe,I t−1), s.t. Le b,t≤1 Rl b,t ξb,tae t, (16) Z1 0(Le b,t−hlSl b,t−1) ηl−1 ηldb ηl ηl−1= (Dl t)e, (17) (Ye t)w=At(Ke t−1)α(Ne t)1−α, (18) log(At) = ρalog(At−1) + σaεa,t, (19) Ke t= (1−δ)Ke t−1+Ie t 1−γ 2 Ie t Ie t−1 −1!2 , (20) PtCe,I t+Z1 0Rl b,t−1Le b,t−1db ≤Pw t(Ye t)w−WtNe t−PtIe t+ (Dl t)e+Ξt+Ψt. (21) The wholesale goods are produced via the technology in Equation (18), where α is the capital share in production, (Ye t)w denotes wholesale goods, and Ne t is labor. The total productivity At is assumed to be the same across entrepreneurs and follow the AR(1) process as in Equation (19), with ρa∈( 0, 1 ) being the persistence of the shock and εa,t following an i.i.d. process with standard deviation σa . Equation (20) is the evolution of capital with, δ∈( 0, 1 ) being the depreciation rate of physical capital and Ie t being entrepreneur e’s investment. Following (Galí and Monacelli 2016), we assume that investment is subject to an adjustment cost function. 11 Entrepreneur e ’s budget constraint is given by Equation ( 21), where Pw t denotes the wholesale price at which entrepreneur e sells its goods in a Economies 2021,9, 203 7 of 26 competitive market to domestic retailers, and WtNe t is the wage bill. Ξt and Ψt are two lump-sum transfers given exogenously to entrepreneurs. 12 The first-order conditions yield: 1 CI t−hICI t−1 −βIEthI CI t+1−hICI t =λI t, (22) βIEt λI t+1 Πt+1 Rl t+νI tRl t=λI t, (23) wt= (1−α)mct ph,p t AtKα t−1N−α t, (24) λI t=λI tqt"1−γ 2It It−1 −12 −γIt It−1It It−1 −1#(25) +βIEt"γλI t+1qt+1It+1 It2It+1 It −1#, λI tqt=βIαEt λI t+1 mct+1 ph,p t+1 At+1Kα−1 tN1−α t!+βI(1−δ)Et(λI t+1qt+1) + νI tξtEt(qt+1Πt+1), (26) where qt=Qt Pt is the price of installed capital measured in units of consumption goods. Note that this price must be equal to the shadow price of capital in units of consumption goods, which means that the equation qt=κI t λI t holds at all times. λI t , κI t , and νI t denote the Lagrange multipliers associated with the budget constraint (21), the law of motion for capital (20), and the collateral constraint (9), respectively. ph,p t=Pt PH,t denotes the relative price, while mct=Pw t PH,t is the real marginal cost of domestic retailers in terms of final goods prices. A combination of Equations (22) and (23) yields a standard Euler equation. Equation (24) describes the optimal choice for labor, which equalizes the marginal product of labor with the marginal cost of labor. Equation (25) characterizes the optimal decision for investment, equalizing the marginal cost of investment to its marginal benefit. Finally, Equation (26) indicates that the cost of acquiring one extra unit of capital equalizes the expected value of price plus the payoff from holding capital. The latter, in turn, integrates the marginal product of capital with the ability to pledge as collateral. 2.3. Retailers There is a continuum of retailers indexed by j∈[ 0, 1 ] . In each period t , retailer j buys the homogeneous wholesale goods from domestic entrepreneurs at the wholesale price Pw t in a competitive market, differentiates them at no cost, and sells them in a monopolistically competitive market at the price PHj,t . The total domestic final good is a composite of individual retail goods: Yt=Z1 0Y e−1 e j,tdje e−1 , where Yj,t is the output of firm j and Yt indicates the total final goods. 13 To introduce price stickiness, we allow for monopolistic competition to occur at the retail level, as in (Bernanke et al. 1999).14 Specifically, we use a Calvo pricing setting with the degree of price stickiness θH , which means that in each period t the retailer j can re-optimize their price Economies 2021,9, 203 8 of 26 with a constant probability 1 −θH . Therefore, the probability that the price set at time t will still hold at time t+sis θs H. The problem of domestic retailer jcan be written as follows: max ¯ PH,t ∞ ∑ s=0 Et[θs HΛp t,t+s(¯ PH,t−Pw t+s)Yj,t+s], (27) s.t. Yj,t=¯ PH,t PH,t−e Yt, (28) where Λp t,t+s denotes a relevant stochastic discount factor for retailers. Since retailers are owned by households, the discount factor is given by Λp t,t+s= (βp)sλp t+s λp t 1 Πt+s . Equation (27) indicates the discounted profits of domestic retailer j , while Equation (28) represents the demand for retailer j ’s goods. The first order condition with respect to ¯ PH,t yields the following: ¯ PH,t=e e−1 Et∑∞ s=0(θH)sΛp t,t+s(PH,t+s)e+1Yt+smct+s Et∑∞ s=0(θH)sΛp t,t+s(PH,t+s)eYt+s , where mct+s=Pw t+s PH,t+s denotes the real marginal cost of domestic retailer jin terms of final goods price at period t+s ; e e−1> 1 is the markup earned by retailers. Since all retailers who can re-optimize their prices at time tchoose the same price, the aggregate price index of final domestic goods evolves according to: PH,t= [θHP1−e H,t−1+ (1−θH)( ¯ PH,t)1−e]1 1−e. Therefore, the inflation rate of domestic goods is: ΠH,t=PH,t PH,t−1 =hθH+ (1−θH)¯ Π1−e H,ti1 1−e, (29) where ¯ ΠH,tis defined as ¯ ΠH,t=¯ PH,t PH,t−1. 2.4. Banks The economy is inhabited by a continuum of banks indexed by b . They receive deposits ( Mi b,t ) from households, borrow from foreign countries, and use these funds to lend to entrepreneurs ( Le b,t ). Following (Schmitt-Grohé and Uribe 2003), we employ the debt elastic interest rate to close our open economy model and induce stationarity. Specifically, we assume that banks borrow from foreign countries ( Db,t ) at an interest rate Rf t . The rate Rf t , in turn, rises in the aggregate level of debt and is assumed to take the following form: Rf t=R∗+¯ p+ϕ(e ˜ Dt P∗ t −D P∗−1), where R∗ denotes the (gross) world interest rate, which is assumed to be constant for simplicity, ¯ p captures the invariant component of a country-specific interest rate premium, and the remaining term is the variant component of the premium. The variable ˜ Dt denotes the aggregate level of foreign debt, which is taken as exogenous by the bank, and P∗ t is the foreign price index. 15 The parameter ϕ measures the elasticity of domestic interest rate with respect to changes in the external debt. In each period t , the individual bank b chooses foreign debt Db,t , the total amount of loans Lb,t , its lending rate Rl b,t , and its LTV ratio ξb,t to maximize its expected discounted profits. 16 Since banks are owned by households, the discount factor Λp t,t+s is also given by households’ marginal rate of substitution. The maximization problem of the bank can be written as follows: Economies 2021,9, 203 15 of 26 deep habits in the banking sector and the model without the deep habits mechanism. Figure 1presents the IRFs for a number of key variables to monetary policy shock. After a contractionary monetary policy shock, output, consumption, and investment decrease. Furthermore, the bank spread between the lending rate and the policy rate in the baseline model goes up due to the presence of deep habits. The mechanism is the following: When the shock arrives, output and demand for loans are both lower than usual. As a result, the future market share motive is dominated by the current profit one and banks try to exploit the lending relationship by increasing the bank spread to raise their current profit. The cost of lending increases, and thus investment decreases more in the baseline model than that in the model without the deep habits mechanism. In turn, output, consumption, and employment all drop by more under the deep habits model. The model thus generates the countercyclical movement of the bank spread, which is in line with previous literature. In addition, banks also tighten up the other credit standards, the collateral pledge. Specifically, the monetary policy shock causes a drop in the LTV ratio, which expresses an increase in the collateral requirement. The demand for loans, and thus investment, output, and consumption fall even further in the deep habits model. The countercyclical fluctuations in both interest rate spreads and collateral requirements amplify the propagation of the monetary policy shock to the economy. Figure 1. Impulse responses to the monetary policy shock, εr,t of size one standard deviation in two different models: deep habits model (baseline) and no deep habits model. As for the responses of openness-related variables, the monetary policy shock triggers contractions in domestic demand for foreign output, generating an increase in trade balance and a decrease in foreign debt in both models. This is supported by both empirical evidence (see, e.g., (Lindé 2003)) and theoretical models (see, e.g., (Christiano et al. 2011); (Kollmann 2001)). It is also worth noting that under the baseline model, foreign debt decreases more than in the no deep habits model. The explanation is as follows: Foreign debt is one source Economies 2021,9, 203 16 of 26 of funding that banks can utilize to offer loans to entrepreneurs and under the deep habits mechanism, total lending drops by more, the demand for foreign borrowing thus drops even further. The dynamic effect of the foreign demand shock is presented in Figure 2. A positive foreign demand shock pushes up the foreign demand for domestic goods, and thus the exports. This drives up the production level of entrepreneurs, making them raise their demand for loans from banks, and thus also their demand for capital stock and labor. Because of the shock’s persistence, output and demand for loans are expected to be high in the periods to come. Under the baseline model featuring the lending relationship, banks lower the interest rate spread and relax the credit constraint since current profit is not a priority at present. Therefore, investment, output, employment, and total lending increase by more in the presence of deep habits. Furthermore, it may seem surprising that foreign debt negatively correlates with output and the trade balance exhibits pro-cyclical behavior. The reason is that the foreign demand shock induces an increase in foreign assets, thus generating a decrease in foreign debt and an increase in trade balance (see, e.g., (Lim and McNelis 2008)). Additionally, note that foreign debt falls by less in the baseline model in order to support a larger increase in total lending. Figure 2. Impulse responses to the foreign demand shock, εy,t of size one standard deviation in two different models: deep habits model (baseline) and no deep habits model. The IRFs in Figure 3for the technology shock can be characterized as follows: A positive technology shock leads to an increase in consumption, investment, and output. Under the deep habits model, banks lower the lending margin and raise the LTV ratio. The explanation is that after the positive shock, output and demand for loans will be higher for periods to come due to the persistence of the shock, and thus the future profits of banks are expected to be higher. Consequently, banks are willing to sacrifice current profit for future market share by relaxing both the bank spread and the LTV ratio. Under the baseline Economies 2021,9, 203 17 of 26 model, the cost of lending falls, and thus investment increases by more than in the model without the deep habits mechanism. Since the increase in investment positively affects the capital stock, output is raised by more in the baseline model. In addition, the positive technology shock induces an increase in domestic demand for foreign output, triggering a rise in imports. This leads to a fall in the trade balance and a rise in foreign debt. Under the presence of deep habits, the foreign debt increases by more in the baseline model. Similar results are obtained for a positive labor supply shock and the impact is illustrated in Figure 4. A positive labor supply shock drives up both the entrepreneurs’ stock of capital and their demand for labor in current as well as future periods due to the persistence of the shock. The increase in the production level results in a higher demand for loans. Under the deep habits model, a fall in lending margin combined with an increase in the LTV ratio raises the entrepreneurs’ demand for loans further. This is again the result of the deep habits mechanism: the future market share motive dominates the current profit motive since output is expected to be higher in future periods. Banks thus find it optimal to relax credit requirements and lower the bank spread. Analogous to the technology shock, the positive labor supply shock generates domestic demand expansion, resulting in a pro-cyclical response of foreign debt and a countercyclical response of trade balance. In addition, due to the existence of deep habits, the foreign debt increases by more in the baseline model as in the case of technology shock. Figure 3. Impulse responses to the technology shock, εa,t of size one standard deviation in two different models: deep habits model (baseline) and no deep habits model. Economies 2021,9, 203 18 of 26 Figure 4. Impulse responses to the labor supply shock, εz,t of size one standard deviation in two different models: deep habits model (baseline) and no deep habits model. In conclusion, it is shown that for each shock in our model, the endogenized lending relationship works as a financial accelerator of macroeconomic shocks. 4.3. Deep Habits and Aggregate Fluctuations Following (Ravn 2016), we now compare the theoretical variance of macroeconomic variables in two models: the baseline model and the model without the deep habits mechanism. To facilitate the comparisons, we compute the variance ratios for macroeconomic variables by dividing the variance of each variable in the baseline by that of its counterpart in the model without deep habits. The results are shown in Table 3. It is clearly indicated that endogenous credit standards generate a significant increase in macroeconomic volatility. Specifically, the variance of consumption rises by 16% when the deep habits mechanism is incorporated, while that of output increases by somewhat more (21%). The variance of employment increases by 20% due to the positive effect of output on the demand for labor. Moreover, the numbers show that the main source driving up the volatility of output is the investment volatility which goes up by 76% under the deep habits model. This is the direct effect of the endogenous fluctuation in credit standards: during a boom the credit constraints are inclined to be reduced, which enables entrepreneurs to borrow more from the banks. The entrepreneurs, facing collateral constraints, employ the additional funding to push up the capital stock because the capital investment in turn can be used as collateral pledge to increase the access to credit further. This demonstrates how endogenous credit standards work as financial accelerators of macroeconomic innovation in our open economy setting. Economies 2021,9, 203 19 of 26 Table 3. Deep habits and Aggregate fluctuations. Variance Ratio Output 1.21 Total investment 1.76 Total consumption 1.16 Lending 1.32 Employment 1.20 Foreign debt 1.22 Trade balance 1.03 Notes: We use the theoretical variance of selected variables to compare two models. The variance ratios are then computed by dividing the variance of each variable in the baseline by that of their counterpart in the no deep habits model. 4.4. Robustness Check In this section, we check the robustness of our model results presented in the previous section when the values of key parameters are adjusted. In addition, we compare our findings to those shown in the related literature.30 We first examine the sensitivity of our findings to changes in the elasticity of credit risk with respect to the difference between the LTV ratio and its steady-state, Θ . In particular, we let this parameter vary from the baseline value of − 1.5 to a (numerically) smaller value of − 1, as well as to a (numerically) larger value of − 2. The resulting variance ratios for this experiment are displayed in Table 4. It is clearly seen that our results are not sensitive to this elasticity since only minor changes are recorded as we change the value of Θ from − 1 to −2.31 We next consider the robustness of our results to changes in the persistence of deep habits in banking, as captured by the parameter ρl . As the number illustrates, changing ρl results in significant changes in the variance ratios of macroeconomic variables. In addition, a low persistence of deep habits of 0.3 is sufficient to eliminate the additional volatility emerging from the lending relationship. Therefore, a certain degree of persistence is needed to observe any amplification. Similarly, changing the strength of deep habits hl leads to major changes in our results, and a reduction of this parameter to 0.3 is enough to remove the amplification arising from deep habits in banking. The next two columns display the results for different elasticity of substitution between banks’ loans ηl . It is clear that changing the value of ηl does not alter our conclusions from the baseline model. Specifically, a reduction of this value to 190, as in (Aliaga-Díaz and Olivero 2010), leads to even stronger amplification, while with a higher value of 300, the model still displays substantial amplification. Table 4. Robustness check. Variance Ratio Output Investment ConsumptionLending Employment Debt Trade Balance Baseline 1.21 1.76 1.16 1.32 1.20 1.22 1.03 Θ=−1 1.21 1.76 1.16 1.31 1.20 1.22 1.03 Θ=−2 1.22 1.76 1.17 1.32 1.20 1.22 1.03 ρl=0.3 1.01 1.14 1.01 1.03 1.01 1.00 0.95 hl=0.3 1.00 1.06 1.00 1.02 1.00 0.99 0.96 ηl=190 1.28 1.98 1.22 1.40 1.26 1.29 1.06 ηl=300 1.15 1.54 1.11 1.23 1.14 1.15 1.00 η=1.5 1.20 1.74 1.13 1.32 1.19 1.18 0.94 η=2.5 1.22 1.77 1.18 1.31 1.21 1.27 1.09 v=0.3 1.22 1.76 1.17 1.30 1.20 1.23 1.04 v=0.4 1.21 1.76 1.16 1.32 1.20 1.22 1.02 Notes: We use the theoretical variance of selected variables to compare two models. The variance ratios are then computed by dividing the variance of each variable in the baseline by that of their counterpart in the no deep habits model. Economies 2021,9, 203 20 of 26 As shown in the Table 4, we also report the robustness check for openness-related parameters. Given the uncertainty in the literature about the value of trade elasticity of substitution η , we allow this parameter vary from its baseline value of 2 to 1.5 or 2.5. It is clearly shown that variance ratios of macroeconomic variables displays only minor changes as we change the value of trade elasticity from 1.5 to 2.5. A similar result is obtained when we let the degree of openness v vary from 0.3 to 0.4. Changing v only leads to a very small deviation from our baseline results. 4.5. Policy In the previous section, we have demonstrated that the fluctuation in credit standards serves as a financial accelerator of the business cycle. This suggests that while constructing policies to protect the economy from unfavorable financial market spillovers, policymakers should take this mechanism into consideration. In this section, we examine three alternative monetary policies that may be employed to alleviate the effect of endogenous credit standards.32 log Rd t Rd!=ρrlog Rd t−1 Rd!+ (1−ρr)φπlogΠt Π+φllogltΠt lt−1Π+εr,t, (50) log Rd t Rd!=ρrlog Rd t−1 Rd!+ (1−ρr)"φπlogΠt Π+φrlog Rf t Rf!#+εr,t, (51) log Rd t Rd!=ρrlog Rd t−1 Rd!+ (1−ρr)φπlogΠt Π+φelogext ex +εr,t, (52) where φl , φr , and φe denote the responses of the policy rate to the nominal credit growth, the deviation of the foreign interest rate from its steady-state, and the deviation of the exchange rate from its steady-state, respectively. Equation (50) is a credit growth augmented monetary policy; Equation (51) is a foreign interest rate augmented monetary policy; and Equation (52) is an exchange rate augmented monetary policy. 4.5.1. Aggregate Fluctuations We first illustrate how different monetary policies might be used to mitigate the influence of the deep habits mechanism on aggregate fluctuations. To this end, we compare the theoretical variances of selected macroeconomic variables in the model without the deep habits mechanism with those in: (1) the deep habits model with credit growth augmented monetary policy; (2) the deep habits model with foreign interest rate augmented monetary policy; and (3) the deep habits model with exchange rate augmented monetary policy, respectively. The results are presented in Table 5. Some interesting findings are obtained from this exercise. On the whole, these three policies do reduce the impact of deep habits on aggregate fluctuations. Second, the effectiveness of these policies increases when we increase the policy parameters ( φl , φr , φe ). For example, let us consider the results of credit growth augmented monetary policy. The variance ratios of output for φl= 0.01 and φl= 0.05 are 1.18 and 1.06, respectively, indicating the credit growth policy is more effective in reducing the effect of the deep habits mechanism when we increase φl . Similar conclusions are drawn for the other two policies. Last, all three policies, if well-constructed, can eliminate the majority of the additional fluctuations deriving from deep habits in the banking sector. Economies 2021,9, 203 21 of 26 Table 5. The impacts of different monetary policies. Credit growth augmented monetary policy Variance ratio φl=0.01 φl=0.05 φl=0.07 φl=0.1 Output 1.18 1.06 1.01 0.94 Total investment 1.71 1.52 1.44 1.33 Total consumption 1.14 1.03 0.99 0.93 Lending 1.28 1.16 1.11 1.04 Employment 1.16 1.04 0.98 0.91 Foreign debt 1.18 1.06 1.01 0.93 Trade balance 1.00 0.90 0.86 0.80 Foreign interest rate augmented monetary policy Variance ratio φr=0.01 φr=0.05 φr=0.07 φr=0.1 Output 1.19 1.11 1.07 1.01 Total investment 1.72 1.59 1.53 1.44 Total consumption 1.15 1.08 1.05 1.00 Lending 1.29 1.19 1.14 1.08 Employment 1.18 1.09 1.05 0.99 Foreign debt 1.19 1.10 1.06 1.00 Trade balance 1.01 0.94 0.90 0.86 Exchange rate augmented monetary policy Variance ratio φe=0.01 φe=0.05 φe=0.07 φe=0.1 Output 1.19 1.10 1.06 1.00 Total investment 1.72 1.59 1.52 1.43 Total consumption 1.14 1.07 1.04 0.99 Lending 1.30 1.24 1.21 1.16 Employment 1.17 1.08 1.03 0.97 Foreign debt 1.19 1.09 1.04 0.97 Trade balance 1.00 0.91 0.87 0.81 Notes: We use the theoretical variance of selected variables to compare two models. The variance ratios are then computed by dividing the variance of each variable in the credit growth (foreign interest, and exchange rate) augmented monetary policy by that of their counterpart in the no deep habits model. 4.5.2. Impulse Response Analysis We now consider the impulse response functions. To facilitate the comparisons, we display the impulse response functions for three models: (1) the baseline model; (2) the model without the deep habits mechanism; and (3) the deep habits model with credit growth augmented monetary policy. In Figure 5, we demonstrate the impacts of nominal credit growth augmented monetary policy in our model. The figure displays the impulse responses of selected variables to a positive technology shock in the baseline model and the model without deep habits, as well as the impulse responses generated when the baseline model is incorporated with the policy (50) with a value of φl= 0.1. It is seen that the introduction of credit growth augmented monetary rule actually reduces the effect of endogenous credit standards on macroeconomic fluctuation significantly. 33 The explanation is that in the presence of interest rate responsiveness to nominal credit growth, the central bank can partially counteract the fluctuations of lending. Therefore, compared to the baseline model in which the simple monetary policy rule is applied, less credit is injected into the economy during the boom. Economies 2021,9, 203 22 of 26 Figure 5. Impulse responses to the technology shock, εa,t of size one standard deviation with and without the credit growth augmented policy. 5. Conclusions In the present study, we augmented a small open economy model with three financial frictions: monopolistic competition, borrowing constraints, and lending relationships. Following (Aliaga-Díaz and Olivero 2010), we assumed that entrepreneurs form deep habits in their demand for banks’ loans to incorporate the lending relationship in our framework. In this way, fluctuations in credit standards were endogenized, as in (Ravn 2016). In order to extend the current setting to the small open economy framework, we made use of the model of (Galí and Monacelli 2016) with some modifications. First, we allowed for a Calvo-type price setting of imported goods to incorporate the incomplete pass-through into the model, as in (Monacelli 2005). Second, we relaxed the assumption of a complete international asset market and used the debt elastic interest rate to close the model and induce stationary. We then employed this framework to analyze how endogenous credit standards amplify the propagation mechanism of macroeconomic shocks to the economy. Our analysis has demonstrated that countercyclical movements in credit standards indeed work as an amplifier of shocks to the economy. In particular, the existence of endogenous credit standards increases output volatility by approximately 21%. Furthermore, we suggested three alternative tools for policymakers to mitigate the impact of endogenous credit standards on macroeconomic volatility. First, we have shown that credit growth augmented monetary rule succeeds in counteracting the fluctuation of lending, and thus considerably decreases the additional volatility. Second, the exchange rate augmented monetary policy, if well-constructed, is considered an efficient tool to eliminate most of the additional fluctuations caused by deep habits in the banking sector. Finally, the introduc- Economies 2021,9, 203 23 of 26 tion of the foreign interest augmented monetary rule also proved successful in dampening the effect of endogenous movements in lending standards. For future research, we plan to incorporate housing into household consumption and the production function of entrepreneurs. The Ref. (Liu et al. 2013) found that the movements in land prices and the quantity of land are crucial factors in explaining the business cycle, and that a large portion of the investment fluctuation can be attributed to a shock to land prices. The Ref. (Ravn 2016) investigated the impacts of commercial land on aggregate fluctuations, and demonstrated that excluding land from entrepreneurs’ production function reduces additional volatility emerging from the deep habits mechanism. Therefore, we anticipate that adding housing to our model can further amplify the effects of endogenous credit standards on the economy. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are publicly available. Acknowledgments: The author would like to thank Shinichi Nishiyama, Shuhei Takahashi, Munechika Katayama, Takayuki Tsuruga, participants at the International Conference on Trade, Financial Integration and Macroeconomic Dynamics & IEFS Japan 2019 Annual Meeting, the Kyoto University Macroeconomics Study Group 2020, the Kobe Macroeconomics Study Group 2020, the BBL workshop, Graduate School of Economics, Kyoto University 2019, three anonymous referees, and the editor for their helpful comments and suggestions. All the remaining errors are, of course, my own. Conflicts of Interest: The author declares no conflict of interest. Notes 1 Several studies support this finding. For example, the Ref. (Aliaga-Díaz and Olivero 2010) points out that banks obtain an information monopoly over the creditworthiness of customers when they monitor borrowers, which triggers costs for borrowers to switch to other banks (switching costs). Deep habits, as proposed by (Ravn et al. 2006), can be indicated as a parsimonious way of incorporating switching costs into the dynamic general equilibrium model. Furthermore, the deep habits model can generate countercyclical credit standards, which is in line with empirical findings. The explanation is that an expansionary shock triggers an increase in output, and thus the demand for loans from entrepreneurs. In order to set the new bank spread, banks will consider the following trade-off: (1) Increasing the current profit by setting a higher spread; (2) generating a higher future market share by lowering the spread to attract more borrowers. Due to the persistence of the shock, the latter effect dominates the former one. As a result, a positive shock will lead to a lower credit spread. 2 We believe that bank competition on the amount of collateral that firms need to pledge is particularly relevant for the market of bank loans. The Ref. (Cerqueiro et al. 2016), using the difference-in-difference method for Swedish data, demonstrated that collateral is crucial for both borrowers and lenders and that with a high-quality collateral pledge, borrowers can experience a lower lending rate and an increase in credit availability. Furthermore, as the duration of the lending relationship increases, collateral requirements tend to relax. The Ref. (Berger and Udell 1995), for example, showed that a long relationship with banks reduces the probability of collateral pledging for borrowers. 3 For this study, we choose the Swedish economy to calibrate our models for the following reasons: First, Sweden is a small open economy with a floating exchange rate regime; thus, the fluctuation of the exchange rate could play an important role in the formulation of monetary policy. The Ref. (Bjørnland and Halvorsen 2014) provided empirical evidence to support this hypothesis. Specifically, by employing a structural VAR model with sign and zero restrictions, they found that monetary policy responds strongly to the movements in the exchange rate in the case of Sweden. Second, empirical studies demonstrate that credit standards in Sweden are countercyclical (see (Olivero 2010)). Therefore, investigating the impacts of credit standards on aggregate fluctuations in the Swedish economy is extremely important. Finally, as previously indicated, bank competition over the amount of collateral that entrepreneurs must pledge is particularly relevant for the Swedish bank loan market. 4 Various empirical studies support the viewpoint of Monacelli (see, e.g., (Campa and Goldberg 2005); (McCarthy 2007)). Moreover, the Ref. (Ferrero et al. 2008), using data from Sweden, Italy, and the United Kingdom, provided evidence on how import prices react to a sharp initial depreciation of the exchange rate and conclude that the pass-through on import prices is high, but with a delay. Economies 2021,9, 203 24 of 26 5 Note that Ci,p H,t and Ci,p F,t are, in turn, composites of domestic differentiated goods and imported differentiated goods indexed by j∈(0, 1): Ci,p H,t=  1 Z 0 (Ci,p Hj,t)e−1 edj  e e−1 ,Ci,p F,t=  1 Z 0 (Ci,p Fj,t)e−1 edj  e e−1 . 6 The derivations of first-order conditions are available upon request. Following the standard literature in models with deep habits, we consider a symmetric equilibrium only. 7 For simplicity, we assume that the share of imported goods in the consumption basket of entrepreneurs is the same as that of households. 8For simplicity, it is assumed that the LTV ratio allowed by bank bis the same for all entrepreneurs. 9 There are a number of reasons for entrepreneurs to minimize their collateral pledges. One crucial reason is that they do not want to lose control of assets in the event of default. Moreover, the process of asset valuation induces some additional costs, which entrepreneurs would prefer to avoid. It is worth noting that when vb is equal to zero, entrepreneurs are just concerned with the interest rate expenditure, as in (Aliaga-Díaz and Olivero 2010). 10 The actual borrowing, denoted by Le b,t , is the amount that entrepreneur e will pay back to bank b in the following period. The effective loan, instead, demonstrates the fund available to that entrepreneur after deducting the switching costs to pay for investments, labor services, and so forth. 11 Analogous to consumption, investment is a composite index of domestic and foreign goods, that is, Ie t= [( 1 −v)1 η(Ie H,t)η−1 η+ v1 η(Ie F,t)η−1 η]η η−1 . Ie H,t and Ie F,t are, in turn, composite indexes of domestic and imported differentiated goods, respectively. Following (Galí and Monacelli 2016), we assume that the share of imported goods in the investment basket is the same as that in the consumption basket for simplifying reasons. 12 Ξt≡hlR1 0 ξb,t ξtSl b,t−1db represents the difference between effective and actual borrowings, while Ψt≡R1 0( 1 −χb,t−1)(Rl b,t−1Lb,t−1− τξt−1at−1)db indicates the wedge between the effective and actual repayment of loans. χb,t is the probability of repayment and the parameter τ captures the fact that the value of the collateral is lower in liquidation, as we discuss in more detail in the banking sector. The two lump-sum transfers are to guarantee that all markets clear. 13 Since we assume that retailers involve no cost at differentiating goods and that each retailer is matched to one entrepreneur randomly, the following equation must hold for all time t:Ye t=Yj,t. 14 This assumption renders our analysis of entrepreneurs’ problems simpler, as pointed out by (Bernanke et al. 1999). 15 In the symmetric equilibrium, we have ˜ Dt=Dt. 16 The total amount of loans Lb,tis defined as Lb,t=R1 0Le b,tde. 17 Following (Ravn 2016), we assume that a fraction of bank b ’s loans to a specific entrepreneur relative to the total loans of that bank Le b,t−1 R1 0Le b,t−1db  is equivalent to a fraction of that bank’s total loans relative to the total loans of all banks in the economy Lb,t−1 R1 0Lb,t−1db  . Furthermore, it is assumed that a lump-sum transfer Ψt is made to entrepreneurs for compensation of handing over their assets in order to guarantee that no money falls out of the economy. 18 For simplicity, we assume that entrepreneurs do not internally consider that they can not pay back the loans with some positive probability. This implies that when offering loans to entrepreneurs, the bank recognizes that a proportion of the total loans will end up not being repaid ex-post, while each entrepreneur simply thinks that they can repay the loan they obtain. The wedge between effective and actual repayment of loans arising from this assumption ends up in the lump-sum transfer earned by the entrepreneur. The more credit standards are relaxed, the larger the wedge becomes. 19 Each bank also faces a trade-off when choosing its lending rate: while raising the lending rate Rl b,t leads to higher profits, it comes at the cost of losing market share as entrepreneurs switch to other banks. 20 We impose the condition P∗ Fj,t=P∗ F,t , for all t because prices are assumed to be flexible in the world economy. Therefore, the marginal cost is the same for all importers as in (Monacelli 2005). 21 Note that Lgt measures the deviation from the law of one price. When we shut down the incomplete pass-through feature, the law of one price holds, that is, Lgt=1 for all t. 22 This assumption is reasonable because the Sveriges Riksbank (the central bank of Sweden) has introduced the inflation target since 1993. 23 The aggregate export C∗ H,t is produced via the following technology: C∗ H,t=hR1 0(C∗ Hj,t)e−1 eie e−1 , where C∗ Hj,t is the export good j . 24 In fact, we consider a semi-symmetric equilibrium as in (Airaudo and Olivero 2019). On one hand, we assume that all households in the consumption sector, all wholesale goods entrepreneurs in the producing sector, and all banks in the financial sector do behave identically. On the other hand, we assume that price is sticky in the retail sector. Specifically, there exists a faction of 1 −θH of retailers that can reoptimize their prices, whereas a fraction of θH cannot. As a result, the pricing is different among