metadata: authors: 0: author: Barattieri, Alessandro
1: author: Eden, Maya
2: author: Stevanovi, Dalibor
count: World
docna: 0: docna: The connection between Wall Street and Main
            Street : measurement and implications for monetary policy
docty: Policy Research Working Paper
theme: Micro, Small and Medium Enterprise support
majtheme: Financial and private sector development
subtopic: Social Funds and Pensions,Public Sector Economics,Banks & Banking Reform,Capital Markets and Capital Flows,Non Bank Financial Institutions,Capital Flows,Macroeconomic Management,Mutual Funds,Public Finance Decentralization and
            Poverty Reduction
teratopic: Macroeconomics and Economic Growth,Finance and Financial Sector Development,Private Sector Development
abstracts: cdata!: This paper proposes a measure of the
            extent to which a financial sector is connected to the real
            economy. The Measure of Connectedness is a measure of the
            composition of assets, namely the share of credit to the
            non-financial sectors over the total credit market
            instruments. The aggregate Measure of Connectedness for the
            United States declines by about 27 percent in the period
            1952-2009. The authors suggest that this increase in
            disconnectedness between the financial sector and the real
            economy may have dampened the sensitivity of the real
            economy to monetary shocks. They present a stylized model
            that illustrates how interbank trading can reduce the
            sensitivity of lending to the entrepreneur's net worth,
            thereby dampening the credit channel transmission of
            monetary policy. The Measure of Connectedness is interacted
            with both a structural vector autoregressive model and a
            factor-augmented vector autoregressive model for the United
            States economy. The analysis establishes that the impulse
            responses to monetary policy shocks are dampened as the
            level of connection declines.
repnme: repnme: The connection between Wall Street and Main
            Street : measurement and implications for monetary policy
display_title: The connection between Wall Street
            and Main Street : measurement and implications for monetary policy
listing_relative_url: /research/2013/10/18412872/connection-between-wall-street-main-street-measurement-implications-monetary-policy
topicv3: Pension Funds m1326066 611,Commercial Banking m1326069 1236,Mutual Funds m1326970 2180,Treasury m1327009 1045,Monetary Policy m1327609 444
publishtoextweb_dt: 2013-10-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Finance and Financial Sector Development,Private Sector Development
no_of_pages: 40
model_extractions: WPS6667 Policy Research Working Paper 6667 The Connection between Wall Street and Main Street Measurement and Implications for Monetary Policy _Alessandro Barattieri Maya Eden Dalibor Stevanovic_ The World Bank Development Research Group Macroeconomics and Growth Team October 2013
Policy Research Working Paper 6667

# **Abstract**

This paper proposes a measure of the extent to which a financial sector is connected to the real economy. The Measure of Connectedness is a measure of the composition of assets, namely the share of credit to the non-financial sectors over the total credit market instruments. The aggregate Measure of Connectedness for the United States declines by about 27 percent in the period 1952–2009. The authors suggest that this increase in disconnectedness between the financial sector and the real economy may have dampened the sensitivity of the real economy to monetary shocks. They present a stylized model that illustrates how interbank trading can reduce the sensitivity of lending to the entrepreneur’s net worth, thereby dampening the credit channel transmission of monetary policy. The Measure of Connectedness is interacted with both a structural vector autoregressive model and a factor-augmented vector autoregressive model for the United States economy. The analysis establishes that the impulse responses to monetary policy shocks are dampened as the level of connection declines.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at meden@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The Connection Between Wall Street and Main Street: Measurement and Implications for Monetary Policy<sup>_∗_</sup> Alessandro Barattieri<sup>_†_</sup> Maya Eden<sup>_‡_</sup> Dalibor Stevanovic<sup>_§_</sup> October 16, 2013

# **JEL classification** : E44, E52, G20

**Keywords** : Connection, financial sector, real economy, monetary policy transmission mechanism

> _∗_ We would like to thank Steve Ambler, Susanto Basu, Ben Eden, Patrick F`eve, Jeffrey Frieden, Marc Giannoni, Alain Guay, Michel Juillard, Sydney C. Ludvigson, Jean-St ́ephane M ́esonnier, Kevin Moran, Louis Phaneuf, Thomas Philippon, Franck Portier, Federico Ravenna, Luis Serven, and seminar participants at ESG UQAM,<sup>`</sup> Bank of France, EUI, Atelier en macro ́economie CIRPEE-DEEP-TSE<sup> ́</sup> 2013 and 2013 CEA annual conference for useful comments and suggestions. Elisa Martin provided outstanding research assistance. This paper does not necessarily reflect the views of the World Bank, its Executive Directors or the countries they represent.

> _†Corresponding Author_ ESG UQAM and CIRP ́EE. Mail: Case Postale 8888, succursale Centre-ville Montreal (Quebec) H3C 3P8. Tel: +1-514-987-3000 (0850#). Fax: +1-514-987-8494. E-mail: barattieri.alessandro@uqam.ca

> _‡_ The World Bank. Macroeconomics and Growth Research Group. 1818 H st. NW. Washington, DC. Tel: +1-857-246-9722. E-mail: meden@worldbank.org

> _§_ ESG UQAM, CIRP ́EE and CIRANO. Mail: Case Postale 8888, succursale Centre-ville Montreal (Quebec) H3C 3P8. Tel: +1-514-987-3000 (8374#). Fax: +1-514-987-8494. E-mail: dstevanovic.econ@gmail.com

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# **1 Introduction**

Two facts constitute the background of this paper. First, the U.S. financial system underwent a radical transformation during the last decades. The complexity and the nature of the process of financial intermediation changed substantially.<sup>1</sup> Figure 1 confirms this well known phenomenon by reporting the evolution of the share of total assets in the U.S. economy held by three major groups of actors: i) the traditional actors (commercial banks, savings institutions and credit unions), ii) the insurance, pension and mutual funds, and iii) the so called _“shadow banking system”_ (Government Sponsored Enterprises (GSE), Assets-backedsecurities issuers, GSE mortgage pools, finance companies, brokers and dealers).<sup>2</sup> While the share of assets held by the traditional actors declined from about 60% to roughly 30% from 1952 to 2010, the share of assets held by the “new” actors increased from almost zero to more than 40% in 2006.

Second, a well known result in the economic literature is that in more recent samples, the sensitivity of real variables to monetary policy shocks has declined. A common explanation for this empirical finding is an increase in the effectiveness of monetary policy, as for example proposed by Boivin and Giannoni (2006). Another frequently conjectured (but less studied) hypothesis is that structural changes in the financial sector contributed to the changing nature of the monetary policy transmission mechanism.<sup>3</sup> Arguably, part of the difficulty in addressing this hypothesis is the lack of a suitable _measure_ of the structural transformation that affected the U.S. financial system.<sup>4</sup> In this paper, we propose such a measure and study its implications for monetary policy. The _Measure of Connectedness_ is a measure of composition of the assets, namely the share of the credit to the non-financial sectors (households, non-financial firms and government) over the total credit market instruments. The aggregate U.S. _Measure of Connectedness_

> 1See Gorton and Metrik (2012).

> 2See Adrian and Shin (2010), Poznar et al (2012) and references therein for a comprehensive explanation of the concept of Shadow Banking.

> 3A notable exception is Dynan et al (2006), who analyses the impact of monetary policy on real activity before and after relevant regulatory changes.

> 4Contributions in this literature analyze the different responsiveness across different sub-samples of the data.

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declines by about 27% in the period 1952-2009. The increase in the disconnection between the financial sector and the real economy implies a larger interconnectedness within the financial sector. We suggest that this increase in interconnectedness within the financial sector may have dampened the sensitivity of the real economy to monetary shocks. We present a stylized model that illustrates how interbank trading can reduce the sensitivity of lending to the entrepreneur’s net worth, thereby dampening the credit channel transmission of monetary policy. We then present some empirical evidence consistent with the predictions of the model.

Importantly, we take here an entirely positive perspective. We are not addressing the question of whether a change in the connection between the financial sector and the real economy is good or bad.<sup>5</sup> We proceed in three steps. First, we propose as measure of the structural transformation of the financial sector, the extent to which it is _connected_ to the real economy. The _Measure of Connectedness_ is the share of the credit market instruments represented by claims whose _direct counterpart_ belongs to the non-financial sectors (households, non-financial firms and government). We compute the measure of connectedness for each major player in the U.S. financial system for the period 1952-2009, as well as an aggregate measure, using data from the Flows of Funds. The 27% drop in the aggregate measure over the period 1952-2009 is mostly driven by a composition effect, namely the rising importance of financial institutions that are relatively “disconnected” from the real economy (such as the issuers of AssetsBacked-Securities).<sup>6</sup> Second, we propose a stylized model to illustrate how interbank trading, by increasing the liquidity of investment projects, reduces the sensitivity of lending to monetary policy shocks. In particular, we focus on the balance sheet transmission mechanism of monetary policy. According to this channel, changes in the nominal rate affect the net present value

> 5We do think that this is a very interesting question and we will explore it in our future research.

> 6We also investigate the relation between our measure and the share of finance in U.S. GDP reported by Philippon (2012). While they capture very different concepts, we interestingly find a very high correlation between a series equal to one minus our measure of connectedness and the share of finance in non-defence U.S. value added.

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of the borrower’s debt obligations and hence his net worth; through various contractual frictions, the change in net worth influences the expected return to lending (for more on this transmission mechanism, see Bernanke and Gertler, 1995). We show that this transmission mechanism is mitigated as investment projects become more liquid. The key to this result is as follows: absent an interbank market for investment projects, banks face a tradeoff between investment and liquidity. Consequently, changes in the expected return to investment (for example, through changes in the net worth of the borrower) may change the tradeoff between investment and liquidity, thus affecting the equilibrium amount of investment. In the presence of a liquid interbank market for investment projects, there is effectively no tradeoff between investment and liquidity: since all projects have positive net present values (NPV), all projects are implemented, regardless of small changes in their NPV induced by changes in monetary policy.

Finally, we interact our aggregate measure of connectedness with a structural vector auto-regression (VAR) for the U.S. economy, and produce impulse responses to a monetary policy shock conditional on different levels of connection. We check the robustness of our results also using a Factor-Augmented VAR (FAVAR) model. We find that the responses of the real variables to a monetary policy shock are dampened as the level of the connection between the financial sector and the real economy decreases.

This paper is linked to several strands of the literature. First, it is related to the literature dealing with measurement of financial intermediation and its characteristics. Philippon (2012) provides evidence on the quantitative importance and the cost of financial intermediation in the U.S. in the last 130 years. Greenwood and Scharfstein (2013) analyze the growth of the share of finance on GDP in the U.S. while Philippon and Reshef (2013) analyze the growth of the share of finance for several developed countries. Philippon and Reshef (forthcoming) propose evidence on the evolution of the wages in the financial industry for the period 1909-2006.<sup>7</sup> Second, this paper is related to the analysis of the relation between financial firms and the real economy. Mesonnier and Stevanovic (2012) use micro-level financial data to construct

> 7See also the survey on Financial Intermediation by Gordon and Winton (2003).

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an aggregate leverage shock and explore its implications for the real variables. Diebold and Yilmaz (2012, 2013) measure the connectedness within financial firms and its contribution to shock transmission. Brunnermeier et al. (2012) analyze the banks’ non-interest income and how this affects their contribution to systemic risk. Hahm, Shin and Shin (forthcoming) analyze the _non-core_ liabilities of Korean banks and their implications for financial vulnerability.<sup>8</sup> We see this paper as complementary to that line of research, since we focus our attention on the composition of assets. Our _Measure of Connectedness_ , in fact, could be interpreted as a measure of the _core assets_ of financial institutions.

Finally, the paper is related to the literature on the monetary policy transmission mechanism. Boivin and Giannoni (2006) report evidence that the effects of monetary policy shocks on real variables are muted in the post-1980 period, and show how this finding can be explained by an increase in the effectiveness of monetary policy. Boivin et al (2011) report FAVAR evidence as well as evidence from DSGE modeling on the change over time of the monetary transmission mechanism. Confirming the results by Boivin and Giannoni (2006), they also find muted responses of real variables to monetary policy innovations in more recent times, and argue that this is mostly accounted for by changes in policy behavior and the effect of these changes on expectations. Adrian and Shin (2011b) consider more in general the role of financial intermediaries in monetary economics.<sup>9</sup> Closer to our spirit, Dynan et al. (2006) present evidence of the reduced responsiveness of several economic aggregates to shocks, dividing the sample before and after important regulatory changes. We contribute to this literature by providing a measure that can capture the structural transformation that the U.S. financial sector underwent and propose a model that rationalizes the decreased sensitivity of real activity to monetary policy due to this structural transformation.

The paper is organized as follows. In Section 2 we introduce our measure of connectedness between the financial sector and the real economy and document its evolution in the U.S. In Section 3 we outline our stylized theoretical model. In Section 4 we show some empirical

> 8See also the discussion of _non-core_ liabilities contained in Adrian and Shin (2011a) 9A recently proposed complementary channel through which changes in the financial conditions can affect the transmission mechanism of monetary policy is the “risk taking channels”, proposed by Borio and Zhu (2012). See also Bruno and Shin (2013).

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evidence consistent with the theory. Section 5 concludes with several suggestions for future research.

# **2 The measure of connectedness**

Our proposed measure of the connection of a financial sector (or a financial institution) to the real economy is based on the composition of its assets. While the exact formula depends on the type of financial institution analyzed, the _Measure of Connectedness_ is conceptually the share of the credit market instruments represented by claims whose _direct counterparts_ belong to the non-financial sectors (households, non-financial firms and government): where _CREDITit_ represents the total amount of credit market instruments of a given institution _i_ at time _t_ , while _CREDIT_ _~~R~~ EALit_ represents the credit market instruments whose _direct counterparts_ are households (i.e. mortgages and consumer loans), non-financial firms (i.e. commercial loans) or the government (i.e. treasuries). Such a measure can be computed for each player in the U.S. financial system using data from the Flows of Funds.

Probably the most important drawback of using flow of funds data is that we are not able to say much about non-balance sheet items, such as derivatives. Since derivatives are typically used as the main example of the disconnectedness between the financial sector and the real economy, we are aware that we are missing an important piece of information, and we therefore consider our results suggestive of an _upper-bound_ to the level of connectedness. However, an advantage of using flow of funds data is that the measure can be computed for a long time series and hypothetically for different financial systems. Moreover, a conceptually similar measure could be computed also for a single financial institution using balance sheet data.

We stress that we try to measure the share of credit market instruments whose _direct counterparts_ are in the non-financial sector. As many commentators observed, the transfor6
mation of the financial intermediation process from the _“traditional”_ banking model to the _“originate and distribute”_ model produced a lengthening of the chain linking the ultimate lender to the ultimate borrower (Adrian and Shin, 2011a). This lengthening may have had several consequences, including the erosion of lending standards (Keys et al, 2010), which likely played an important role in the crisis that started in 2007.<sup>10</sup> In what follows we describe the construction of the measure for each element of the U.S. financial system for the period 1952Q1-2009Q4.<sup>11</sup> We use data from the release Z1 of March 8, 2012. A Data Appendix contains details on the composition of the measure for different players within the financial system.

**Traditional financial institutions.** Traditional financial institutions include Commercial Banks, Savings Institutions and Credit Unions. For commercial banks, at the numerator of our measure we use the sum of Open Market Papers, Treasury Securities, Non-securitized GSE-backed securities, Municipal securities, Non-securitized Corporate Bonds, Bank Loans, Mortgages, Security Credit and Consumer Credit. At the denominator, we use the total amount of credit market instruments<sup>12</sup> . For Savings Institutions, we use a similar measure.<sup>13</sup> For Credit Unions, the Flows of Funds table does not distinguish between securitized and not-securitized bonds. By including all bonds in the numerator of our measure, once again we are conservative and we accept the risk of over-estimating the connectedness with the real economy.

Figure 2 reports the results obtained for the measure of connectedness of the traditional players. All the three indicators are falling over time, from values very close to 1 to values between 0.77 (for the Credit Unions) and 0.84 (Commercial Banks).

> 10Moreover, this lengthening of the credit chain might have increase the vulnerability of the system to sudden drop in trust, which arguably was an important element in explaining the freeze of credit markets in the fall of 2008. See Gennaioli, Shleifer, and Vishny (2012) for an example of a model where trust between actors play an important role in the intermediation process.

> 11A change in the accounting rule governing the GSE at the beginning of 2010 would introduce a spurious element in our calculations.

> 12Flow of Funds series FL.724.005.005, table L.110

> 13See appendix for details.

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**Insurances, mutual funds and pension funds.** As in the case of Credit Unions, in the case of insurance, pension funds and mutual funds, the tables in the Flow of Funds do not distinguish between asset backed securities and other corporate bonds. Once again, the results need to be interpreted as an upper bound for the connection. Insurance companies can be divided into Property-Casualty Insurance Companies and Life Insurance Companies. As for the mutual funds, we only consider the two most important categories: mutual funds and money market mutual funds (MMMF). Figure 3 reports the results we obtain. Property and life insurance appear to remain fairly connected to the real economy all along the period considered. In contrast, we witness a more pronounced increase in the disconnectedness in the case of mutual funds. Interestingly, the reduction in the connectedness of the MMMF seems to start later, but to have a large drop in the years preceding the crisis, where the MMMF balance sheets were increasingly filled with complex securities like mortgage backed securities (MBS).

Figure 4 shows the evolution of the connectedness over time of Pension Funds. We analyze separately private pension funds, local government pension funds, and federal government pension funds. Here the results are starkly different. While private pension funds appear to have become much more disconnected over time from the real economy (starting from the 1970s), the federal government pension funds barely changed, while the local government pension funds started becoming more disconnected during the 1990s.

**Shadow banking.** We consider the connectedness of Government Sponsored Enterprises (GSEs), Brokers and Dealers, Finance Companies and ABS issuers. Finance companies are fully connected to the real economy, and hence we assign a value of 1 to their measure of connectedness. Figure 5 shows our results for the other actors within shadow banking. Not surprisingly, the Brokers and Dealers appear to be fairly disconnected from the real economy ever since they appear in the 1980s. Even more so is the case for the ABS issuers.<sup>14</sup> 14The big drop observed in the series is due to the fact that in the first observation available for ABS issuers in the Flow of Funds (1983q2) the only asset class recorded is trade credit. We consider trade credit as being part of the credit to the real economy, hence the Measure of Connectedness of ABS issuers for that quarter is equal to one.

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More surprising is probably the evolution of the connectedness of the GSEs, who display a downward trend in the 1990s, when they started to invest also in structured financial products.

We exclude from the analysis the Agency and GSE-backed mortgage pools. The reason is to avoid double counting.<sup>15</sup>

**Aggregate measure.** In order to have an aggregate picture of the connectedness of the U.S. financial system to the real economy, we take a weighted average of each measure of connectedness, weighting each actor by its time-varying share of credit market instruments:

where _ωi,t_ = _CREDITCREDITUS,ti,t_<sup>and</sup><sup>_i_=</sup><sup>_{CB, SI, CU, PI, LI, MF, PF, GSE, BRO, FIN, ABS}_.</sup> Figure 6 reports the result. As the figure shows, the aggregate drop in connectedness is of the order of 27% during the period 1952-2009. It is interesting to notice how the aggregate result we obtain for the measure of connectedness is, to a certain extent, due to a composition effect. Figure 7 shows the shares of credit market instruments held by the different types of financial institutions. Similar to Figure 1, the shadow banking share increased greatly in the last 30 years, while the share of the traditional players dropped significantly. Figure 8 reports the results on the share of credit market instruments for two particularly relevant players: commercial banks and ABS issuers. As the figure shows, the drop in the share of credit market instruments held by commercial banks is coupled with an important increase in the share of the ABS issuers, which then dropped substantially during the crisis.

It is also interesting to note how our measure of connectedness shows some relation with key moments in the history of the deregulation of the U.S. financial system. The measure has a clear change in trend in the early 1980s, when several deregulation acts were promoted in

> 15The Agency and GSE backed securities, in fact, are among the _assets_ that we consider to contribute to the disconnectedness of all the other players in the financial system. An alternative treatment would have been to include this category and assign arbitrarily to this actor a level of connection equal to zero by definition. Once again, we have been conservative and decided to present an upper bound estimate of the level of connection.

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the U.S.<sup>16</sup> . Moreover, the aggregate easure of connectedness seems to accelerate its downward trend for few years, starting in 1999. The Financial Modernization Act, also known as the Gramm-Leach-Bliley Act, passed in 1999, repealed the Glass-Steagall of 1933 and removed the separation between the activities of commercial banking and investment banking, thus spurring a wave of mergers and acquisitions in the U.S. financial sector and leading to a transformation of the business model in several U.S. financial institutions.

Finally, it is important to stress again that here we take an entirely positive perspective, and we are not able to say anything about the welfare implications of the increased disconnectedness between the financial sector and the real economy.

**Measure of connectedness and the share of finance in GDP.** It is instructive to investigate the relation between our measure of connectedness and the share of finance in GDP, constructed by Philippon (2012). In Figure 9, we report both a series equal to one minus our measure of connectedness, which is a measure of disconnection between the financial sector and the real economy, and the share of finance in non-defense value added.<sup>17</sup>

While the two series are conceptually different, they are interestingly highly correlated.<sup>18</sup> Our measure of connectedness is a way of representing the structural transformation that affected the U.S. financial system in the last 50 years. Philippon (2012) measures the share of finance in U.S. GDP. One could conjecture that the structural transformation of the U.S. financial sector captured by our measure might have contributed to a reallocation of resources towards finance, thus implying a greater share of finance in GDP. However other factors, such as a sector-specific technological change or the increasing trend toward financial globalization, might also help explaining Philippon’s findings.<sup>19</sup>

> 16For Instance the Depository Institutions Deregulation and Monetary Control Act in 1980 and the GarnSt Germain Depository Institutions Act, in 1982.

> 17While Philippon’s data are at annual frequency, we interpolated them to transform them into a quarterly series.

> 18The correlation between the two series is 0.98.

> 19Another recent explanation of Philippon’s results can be found in Gennaioli, Shleifer and Vishny (2013), who propose a Solow-type growth model augmented with a financial intermediation process featuring a role for trust. In their model, the share of finance over GDP grows over time due to the role of financial intermediation as a tool for wealth management in an environment where the ratio of wealth to GDP grows as the economy approaches its steady state.

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Since this paper focuses mainly on the implications for monetary policy of the structural transformation that affected the U.S. financial system, we focus in what follows on our measure of connectedness, without taking a strong stance on its contribution to the increase in the share of finance in GDP.

# **3 Connectedness and monetary policy: Theory**

We present a stylized model that captures a possible relationship between the connectedness of the financial sector and the sensitivity of real activity to monetary policy. We focus on the credit channel of transmission of monetary policy, and show how a financial sector more disconnected from the real economy implies a lower sensitivity of lending to interest rate changes.

The basic environment is similar to Diamond and Dybvig (1983) and Allen and Gale (2000), with some ex-ante uncertainty. There are three periods, _t_ = 0 _,_ 1 _,_ 2. At time 0, a continuum of banks are endowed with 1 unit of deposits (deposits here are time 0 goods). At that time, a fraction _q_ 0 of banks are also endowed with an investment opportunity: an investment of _I_ units of deposits at _t_ = 0 (where 0 _≤ I ≤_ 1) yields a return of _AI_<sup>_α_</sup> at _t_ = 2. An investment opportunity can be seen as a request of a loan by an entrepreneur. The parameter _A_ is the recoverable part of output, which in principle changes with the entrepreneur’s net worth. In the background, we can think of an entrepreneur with some initial debt obligations, and some net worth ( _N_ ). The parameter _A_ depends on his net worth _N_ and therefore is decreasing with the NPV of his debt obligations (decreasing in _r_ , the interest rate set by the monetary authority).<sup>20</sup> We assume that any enforceability problems are already summarized in _A_ , the recoverable part of output. The banks observe the realization of _A_ before making their lending decision.

In the background, there is a transmission mechanism of monetary policy: _A_ is increasing in the net worth of the entrepreneur _N_ , and _N_ is decreasing in _r_ . We thus focus on the

> 20We do not take a strong stance on the reason why _A_ might depend on the net worth of the entrepreneur. A possible interpretation is that the entrepreneur puts more effort if his net worth is higher. Formally, we are assuming a function _A_ ( _N_ ( _r_ )) with _N_<sup>_′_</sup> ( _r_ ) _<_ 0 and _A_<sup>_′_</sup> _>_ 0.

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balance sheet channel, as described by Bernanke and Gertler (1995).

All banks - those who receive an investment opportunity and those who do not - have access to a storage technology, that transforms one unit of output at time _t_ to one unit of output in time _t_ + 1. For banks without an investment opportunity, this is the only technology. Banks with an investment opportunity choose between saving in storage and investing.

The bank’s time 0 objective function is: where _c_ 1 is consumption goods at time 1 and _c_ 2 is consumption goods at time 2. The parameter _θ_ is a liquidity shock, that takes value _θ_<sup> ̄</sup> _>_ 1 with probability _q_ and value _θ_ = 1 with probability 1 _− q_ . A realization of _θ_ = _θ_<sup> ̄</sup> _>_ 1 can be thought of as whatever might induce banks to prefer liquidity at time 1 as opposed to time 2.

We first solve the model under the assumption that there is no interbank market. Given our definition of connectedness, this is equivalent to a situation of full connectedness, as the banks only deal with entrepreneurs. We will then allow for the possibility of an interbank market that can be thought of as a partially connected environment. It is useful to keep in mind, given our definitions, that a greater disconnectedness _between_ the financial sector and the real economy implies a greater interconnectedness _within_ the financial sector.

**The fully connected environment.** Absent trade between banks, banks choose the amount of storage and investment to maximize their objective function. For banks that do not have an investment opportunity, the problem is trivial: they will store all deposits; if, at period 1, they find that _θ_ = _θ_<sup> ̄</sup> , they will consume all goods at _t_ = 1. Otherwise, they are indifferent between time 1 and time 2 consumption.

For banks with an investment opportunity, the bank’s problem can be written as: 12
s.t.

Note that, if _θ_ = _θ_<sup> ̄</sup> , the bank strictly prefers to consume its storage rather than to store it for period _t_ = 2. If _θ_ = 1, the bank is indifferent between the two options, so we can assume without loss of generality that it consumes all stored goods at time _t_ = 1. Substituting in the constraints, the problem amounts to: Assuming an interior solution, the first order condition is: Note that equation (7) admits an interior solution only if _E_ ( _θ_ ) _> αA_ . In other words, if the expected valuation of liquidity at _t_ = 1 is sufficiently high, it is optimal for the bank to store some deposits. Assuming that this parametric restriction is satisfied, the interior solution is increasing in _A_ , so there is some transmission of monetary policy (that changes _A_ ( _N_ )). In contrast, at the corner solution small changes in _A_ (or in _N_ , or in _r_ ) would not change the bank’s investment decision, and there would be no sensitivity to monetary shocks.

Formally, we define the sensitivity of real activity with respect to monetary policy in this model to be the semi-elasticity of aggregate investment to interest rate changes. We decompose this semi-elasticity as follows: We assume the second term to be positive and the third term to be negative. Then, from equation (8), we get: 13
where _Ic_<sup>_AGG_</sup> is the aggregate investment in the fully connected environment.

**The partially connected environment.** Assume next that a fraction _λ_ of investment opportunities are tradable at time _t_ = 1. This is a very reduced form way of introducing _securitization_ in the model.<sup>21</sup> The securitization is the process through which an illiquid asset can be sold by the bank to a special investment vehicle (SIV). The SIV transform the illiquid asset into securities, by issuing bonds with stratified risk profiles. The banks, at time _t_ = 0, know whether their investment opportunity (if any) is tradable (liquid) or not.

If there are enough banks with _θ_ = 1 who hold stored goods, the price of time 2 goods in terms of time 1 goods is 1.<sup>22</sup> In this case, banks holding tradable projects who realize _θ_ = _θ_<sup> ̄</sup> can sell their investments at time _t_ = 1 at no loss. We treat _λ_ as a proxy for the extent of the possible interconnectedness _within_ the financial system, and thus as a measure of the disconnectedness _between_ the financial sector and the real economy. The maximization problem of a bank that has a non-tradable project is the same as in the fully connected environment. For banks with a tradable project, the ability to sell the project at _t_ = 1 increases the returns to investment. Since banks always weakly prefer to consume at time _t_ = 1, their maximization problem can be written as if they only consume at time 1:

> 21Hobijn and Ravenna (2010) analyze the influence of securitization on monetary policy in the context of a DSGE model. See Moran and Meh (2013) for a DSGE model featuring a shadow banking sector.

> 22For this to be the case, we need to assume enough “liquid demand” for these assets. Formally, a sufficient condition for this to hold for every _λ_ is (1 _− q_ 0)(1 _− q_ ) _> q_ 0 _qA_ . The left hand side is the amount of storage goods in the hand of _θ_ = 1 banks who did not receive an investment opportunity at time 0. These banks hold storage goods, and are indifferent between time 1 goods and time 2 goods. The right hand side is the value of investment opportunities in the hand of banks who had an investment opportunity, and received a liquidity shock that made it optimal for them to sell at _t_ = 1 (given _I_ = 1, each project promises _AI_<sup>_α_</sup> = _A_ units of time 2 goods). We assume this is the case, in order to capture the large demand for securitized assets prevailing in the financial markets, especially before 2007.

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The FOC with respect to _I_ is: For _αA >_ 1, there is no interior solution. We will assume that this is the case. To summarize, we are making the following parametric restriction: Given this assumption, banks with tradable investment projects have a corner solution, in which they invest the maximum amount _I_ = 1. Intuitively, there is no liquidity advantage to storage if the investment project is tradable. Given a corner solution _I_ = 1, the amount of investment does not respond to small changes in _A_ or in _r_ - investment in these banks is unresponsive to monetary policy.

At the aggregate level, note that investment is now given by: The sensitivity to monetary policy is given by: Note that _λ_ = 0 corresponds to the fully connected case. The absolute value of the above expression is decreasing in _λ_ . To see this, note that the absolute value of this expression is <u>(1</u> _−λ_ <u>)</u> _a_ of the form (1 _−λ_ ) _b_ + _λ_<sup>where</sup><sup>_a >_0and</sup><sup>_b <_1.Thederivativewithrespectto</sup><sup>_λ_is:</sup> This result implies that that the sensitivity of aggregate investment to monetary policy is increasing in connectedness. Note that, in this model, our measure of connectedness 15
corresponds to the share of claims at _t_ = 1 for which the direct counterpart is investment projects. The “disconnected” part is claims held by banks on investment projects initiated by other banks.<sup>23</sup> The equilibrium level of connectedness is given by: It is easy to verify that this expression is decreasing in _λ_ .

Moreover, from equation (12), the aggregate amount of lending is increasing in _λ_ ( _∂I∂λd_<sup>_AGG_</sup> _>_ 0). Intuitively, securitization makes lenders less careful about investing in illiquid assets if they can be sold in case of need at time _t_ = 1.

Altunbas, Gambacorta and Marquez-Ibanez (2009), using detailed information on securitization activities for a sample of European banks, find both that securitization shelters banks’ loans from the effect of monetary policy and that it strengthens the capacity of banks to supply loans. Both results hold in the simple model we presented in this section.

# **4 Connectedness and monetary policy: Evidence**

This section presents some time series evidence to explore how _connectedness_ affects the responses of economic variables to monetary policy shocks. We follow both a structural VAR (SVAR) approach as well as a factor-augmented VAR (FAVAR).

**SVAR.** We adapt the approach of Boivin and Giannoni (2006) by including our measure of connectedness as an exogenous variable.<sup>24</sup> The model can be written as follows:

> 23Instead of “purchasing” the investment projects directly, we can think of the interbank market more realistically as a loan market, in which banks with tradable investment projects borrow against _t_ = 2 investment income. Here, “tradable” has the interpretation of “collateralizable”.

> 24While arguably also the connection might be an endogenous variable, the result obtained in figure 6 indicates how the movements in the connection are long-run smooth movements, and thus we believe it can be considered an exogenous when using business cycle frequency data. In addition, the connection is included with lag one.

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where _Yt_ is a _K ×_ 1 vector of endogenous variable, Φ( _L_ ) is a matrix polynomial of order _p_ and _CONNt−_ 1 is exogenous. The reduced form errors, _et_ , are assumed to be linear combinations of structural shocks, _εt_ : with _E_ ( _εtε_<sup>_′_</sup> _t_<sup>) = Σ,adiagonalmatrix.</sup> It is easy to see that the impulse responses to any shock in _εt_ will depend on _CONNt−_ 1. Without loss of generality, assume _p_ = 2 and develop Φ( _L_ ): where Φ1 _,t−_ 1 = (Φ1 + _βCONNt−_ 1). Hence, the impulse response functions (IRFs) are obtained for any level of _CONNt−_ 1 by inverting the previous expression: In practice, the coefficients matrices Φ( _L_ ) and _β_ are estimated by OLS regression on (18), and _H_ is deduced by imposing enough identification restrictions. The IRFs are then easily computed using (19). The confidence bands can be constructed using a parametric bootstrap.<sup>25</sup> Following Boivin and Giannoni (2006), _Yt_ contains the deviation of the natural

- 25 We use the following procedure:

1. Shuffle the time dimension of OLS residuals _e_ ˆ _t_ and get bootstrap innovations _e_<sup>_∗_</sup> _t_

- _t_

2. Using [ _Y_ 1 _, . . . , Yp_ ] as initial values and _CONNt−_ 1, get the bootstrap endogenous variables from 3. Impose the identification restrictions to get _H_ and calculate impulse responses.

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logarithm of quarterly real GDP (GDPQ) from a linear deterministic trend, the annualized rate of change in the quarterly GDP deflator (GDPD), the natural logarithm of the quarterly average of the monthly spot market commodity price index (PSCCOM) and the quarterly average of the Federal Funds Rate (FFR). The exogenous variable _CONNt−_ 1 contains our aggregate Measure of Connectedness. The data ranges from 1959Q1 to 2009Q1.

Four lags are included in the VAR. The identification of structural shocks is achieved by the following recursive ordering: [ PSCCOM, GDPQ, GDPD, FFR ]. Hence, the unexpected monetary policy shock is ordered last in _εt_ . The rotation matrix _H_ is obtained using Choleski ˆ decomposition of the covariance matrix of _et_ . The 95% confidence intervals are computed using 1000 bootstrap replications.

In Figure (10), we compare the impulse responses of elements in _Yt_ to an adverse monetary policy shock when the Measure of Connectedness is high and low, respectively _CONN_ = 0 _._ 98 and _CONN_ = 0 _._ 82. These are the average values of our connectedness measures for the periods 1959Q1-1983Q4 and 1984Q1-2009Q1. The impulse response with confidence bands for each level of connectedness are presented in Figures (11) and (12).

As we can see from Figure (10), at the level of connection of 0.98 the adverse monetary shock generates a decrease in output, which exhibits a hump-shaped response. The price level decreases too, but only after a few quarters (the well known price puzzle phenomenon). When we consider a level of connection of 0.82, instead, we see that the response of the GDP to the same monetary policy shock is now not statistically different from zero. Also the responses of the quarterly GDP deflator and the spot market commodity price index are muted at the lower level of connection. Interestingly, there is no evidence of price puzzle in that case.

In order to assess whether the difference in the impulse response we obtained under different levels of connection is statistically significant, we plot the difference in Figure (13), and we include confidence intervals at 90% significance level. As the figure shows, the impulse responses of GDP and GDP deflator are statistically different under the two scenarios, while the impulse responses of the commodity price index and the federal funds rate are not 18
statistically significantly different.

The results reported in Figure (13) are robust to the inclusion of a time trend in the model, as well as to a different specification of the lag structure.<sup>26</sup>

**FAVAR.** We conduct a more refined exercise inspired by the model from Bernanke et al. (2005). In contrast to standard structural VAR models, factor models have a number of advantages: i) they allow considering large amounts of information potentially observed by agents, and thus minimize the risk of omitted variable bias; ii) they are not sensitive to the choice of a specific data series, which may be arbitrary; iii) they are less likely to be subject to non-fundamentalness issues raised by Forni et al. (2009)<sup>27</sup> ; and iv) they allow us to compute the response of a larger set of variables of interest to identified shocks.

As in the case of SVAR, we introduce our measure of connectedness through interaction terms, in order to obtain impulse response functions that are conditional on a certain level of connectedness. Formally, we consider the following static factor model with latent and observed factors: where _Ft_ is vector of _K_ latent factors and _Rt_ is the observed factor. In our case, _Rt_ is the the Federal Funds Rate, since the objective here is to identify the monetary policy shock. _Xt_ contains _N_ macroeconomic and financial indicators organized into a block of ‘slow-moving’ variables that are largely predetermined to monetary policy, and another consisting of ‘fast moving’ variables that are sensitive to the FED’s rule. The idiosyncratic errors are assumed to be serially uncorrelated.

> 26We omitted the results here, they are available upon request.

> 27If the shocks in the VAR model are fundamental, then the dynamic effects implied by the moving average representation can have a meaningful interpretation, i.e. the structural shocks can be recovered from current and past values of observable series. Forni et al. (2009) argue that while non-fundamentalness is generic of small scale models, it is highly unlikely to arise in large dimensional dynamic factor models.

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In our application, _Xt_ contains _N_ = 108 quarterly time series from Ng and Stevanovic (2012), that run from 1959Q1 to 2009Q1. Data include both macroeconomic variables such as GDP, employment, investment, hours worked, inflation rate as well as financial variables such as credit spreads, loans, etc.<sup>28</sup> The data have been transformed to induce stationarity and are standardized prior to estimation. The _ICp_ 2 information criterion from Bai and Ng (2002) suggests _K_ = 3 latent factors. The lag order of Φ( _L_ ) is set to 4.

The estimation and identification of structural shocks consist of several steps. First, one must impose _Rt_ as an observed factor when estimating _Ft_ . Let _C_<sup>ˆ</sup> ( _Ft, Rt_ ) be the _K_ principal components of _Xt_ . Let _Xt_<sup>_S_be</sup><sup>_NS_‘slow’movingvariables,andletthe</sup><sup>_K_principal</sup> components of _Xt_<sup>_S_be</sup><sup>_C⋆_(</sup><sup>_Ft_).Theestimateoflatentfactorsis</sup><sup>_F_ˆ</sup><sup>_t_=</sup><sup>_C_ˆ(</sup><sup>_Ft, Rt_)</sup><sup>_−_ˆ</sup><sup>_bRRt_where</sup> ˆ _bR_ is obtained by least squares estimation of the regression ˆ The estimation of the loadings is now straightforward by regressing _Xt_ on _F_<sup>ˆ</sup> _t_ and _Rt_ : Λ<sup>_F_</sup> and Λ<sup>ˆ</sup><sup>_R_</sup> .

Second, using _F_<sup>ˆ</sup> _t_ , we estimate (21) as in the case of the SVAR model. Since _F_<sup>ˆ</sup> _t_ can be correlated with _Rt_ , we identify the monetary policy by ordering _Rt_ last. Finally, we invert (21) to obtain factors’ impulse responses, and multiply them by factor loadings to get the IRFs of any element in _Xt_ . While all the impulse responses are available upon request, we present here only a subset of them.

As before, we compare the impulse responses to an adverse monetary policy shock when the connectedness is high and low, respectively _CONN_ = 0 _._ 98 and _CONN_ = 0 _._ 82. In Figure (14) we report the responses of output, investment and employment to an identified monetary policy shock. In all three cases, the responses of real variables to a monetary innovation are muted at lower levels of connectedness. In order to test whether these differences are statistically significant, we compute the difference between the impulse responses and we compute via bootstrap a 90% confidence internal. In Figure (15) we report the results. The

> 28The complete description of the data and their transformation is presented in the appendix.

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impulse responses of GDP, Investment and Employment are statistically different, at least in the first few quarters.

In Figure (16) we report the results for CPI inflation, bank lending and loans and leases. Interestingly, also the response of bank lending and loans and lease are muted in the case of a lower connection between the financial sector and the real economy. This is precisely the mechanism that we highlighted in the previous section. As before, we check the statistical significance of these results by plotting the difference of the impulse responses and the associated confidence intervals. We show the results in Figure (17).

We conclude that the inclusion of our measure of connectedness into a SVAR or a FAVAR for the U.S. economy generates statistically different responses of real variables to monetary policy innovations.

# **5 Conclusions**

This paper documents a declining trend in the share of financial claims whose direct counterpart is in the non-financial sector. The financial sector’s increased ability to buffer idiosyncratic liquidity shocks may have contributed to an increase in investment, and a decrease in the sensitivity of investment to fundamentals such as the borrower’s net worth. In this paper, we illustrate how this may have contributed to the dampening of the responsiveness to monetary policy. However, the implications of this structural change in the financial system may have had implications far beyond this. We outline here several potential avenues for future research that make use of the measure of connectedness.

First, it would be interesting to develop a quantitative macroeconomic model embedding the concept of connection explored in this paper. This could also be used to evaluate the relative importance of the policy behavior and the disconnectedness in explaining the muted responses of monetary policy innovations on economic variables found using more recent samples.

Second, it would be interesting to explore whether the connection between the financial sector and the real economy affects other dimensions of the macro economy (for example, 21
the availability of credit to firms or the vulnerability to financial crisis).

Third, it may be insightful to analyze the dynamics of the connection between the financial sector and the real economy for additional countries, and study how connection was related to performance during the Great Recession. In this sense, it would be interesting also to address “normative” questions, such as the effects of connectedness on welfare.

Finally, and especially for policy purposes, it would be important to go beyond the aggregate perspective we take in this paper and use balance sheet data on single financial institutions to analyze the impact of their connection with the real economy on a range of performance measures. This could also help improve the regulation and monitoring of financial institutions. We plan to pursue these avenues in our future research.

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