- Email: [email protected]
A model of slow recoveries from financial crises
Accepted Manuscript
A Model of Slow Recoveries from Financial Crises Albert Queralto PII: DOI: Reference:
S0304-3932(19)30054-6 https://doi.org/10.1016/j.jmoneco.2019.03.008 MONEC 3098
To appear in:
Journal of Monetary Economics
Received date: Revised date: Accepted date:
30 September 2016 15 March 2019 19 March 2019
Please cite this article as: Albert Queralto, A Model of Slow Recoveries from Financial Crises, Journal of Monetary Economics (2019), doi: https://doi.org/10.1016/j.jmoneco.2019.03.008
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Highlights • Output, productivity and innovation drop persistently following financial crises. • The paper develops model with endogenous TFP growth via innovation subject to financial frictions.
CR IP T
• The model accounts well for the South Korean evidence following the 1997 crisis.
AC
CE
PT
ED
M
AN US
• Financial frictions amplify significantly the TFP and output losses following the crisis.
1
ACCEPTED MANUSCRIPT
A Model of Slow Recoveries from Financial Crises∗ Albert Queralto† Federal Reserve Board
CR IP T
Abstract
Output and productivity fall persistently following financial crises, and innovation efforts also decline in these episodes. After reviewing the evidence, this paper introduces a quantitative macroeconomic model featuring endogenous growth in total factor productivity (TFP)
AN US
through innovation, in which innovators’ financing is subject to frictions. These frictions become more severe as financial sector balance sheets deteriorate, increasing the cost of credit for innovators and thereby lowering the growth rate of TFP. Key parameters are estimated using data from the South Korean 1997 financial crisis. Financial frictions are found to amplify significantly the medium-run TFP and output losses following the crisis.
1. Introduction
ED
JEL classification: E32; E44; F41.
M
Keywords: Business Cycles, Financial Crises, Total Factor Productivity
PT
A stylized fact of financial crises—well-exemplified by developments following the global crisis of 2008—is that their aftermath tends to be characterized by a slow recovery, resulting
∗
CE
in long-lasting damage to economic activity. Reinhart and Reinhart (2010) and Cerra and
AC
This paper is based on my dissertation at New York University. I am grateful to the co-editor Urban Jermann and an anonymous referee whose suggestions greatly improved the paper. I am especially indebted to Mark Gertler for his advice and guidance on this project. I also thank Ozge Akinci, Dario Caldara, Diego Comin, Martin Eichenbaum, Chris Erceg, Jordi Gal´ı, Matteo Iacoviello, Leyla Karakas, Robert Kollmann, John Leahy, Virgiliu Midrigan, Andrea Raffo, Mathias Trabandt, Vivian Yue and seminar participants at various venues for very useful comments and discussions. Alexander Mechanik, Patrick Moran and Dawson Miller provided outstanding research assistance. Financial support from Fundaci´ on Rafael del Pino is gratefully acknowledged. The views expressed in this paper are those of the author, and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of any other person associated with the Federal Reserve System. Earlier versions of this paper have circulated under the titles “A Quantitative Model of Slow Recoveries from Financial Crises” and “Financial Market Frictions, Productivity Growth and Crises in Emerging Economies.” † Division of International Finance, Federal Reserve Board. E-mail: [email protected]
Preprint submitted to Elsevier
March 22, 2019
ACCEPTED MANUSCRIPT
Saxena (2008), among others, have documented highly persistent drops in activity following severe financial crises, with little evidence of an eventual rebound of output back to trend.1 The first goal of this paper is to provide new evidence that may shed light on the behavior of output following financial crises. The paper documents that a large fraction of the highly persistent output declines is accounted for by persistent drops in productivity, and shows that
CR IP T
crises also tend to be associated with sharp slowdowns in businesses’ innovation expenditures. The declines in innovation efforts during banking crises are much sharper than in typical recession episodes, suggesting that the financial tightening that occurs during these crises may act as a drag on business innovation. In turn, the slower pace of business innovation may help account for the sustained productivity losses.
AN US
The second goal of the paper is to develop a quantitative model to address the evidence. The model introduces two modifications to a neoclassical economy. First, it explicitly accounts for the process of innovation as a source of medium-run productivity growth. Second, it includes an agency friction in financial markets that may disrupt the financing of investments in innovation. Thanks to these features, the model can capture a situation in which
M
a worsening of agency frictions—and the consequent tightening of credit—lowers the aggre-
persistent output losses.
ED
gate pace of innovation, thereby reducing trend productivity growth and leading to highly
The model features sustained growth in TFP arising due to an endogenously expand-
PT
ing variety of intermediates, as in Romer (1990). A competitive entrepreneurial sector has access to a technology that allows it to innovate, but lacks the funds to finance the neces-
CE
sary expenditures. To obtain funds, entrepreneurs borrow from banks. The outcome from entrepreneurs’ innovation efforts consists of novel varieties of intermediate goods, which are
AC
then used in final output production. To provide funding to entrepreneurs, banks borrow from both domestic households and
from international creditors. Banks are assumed to be efficient at monitoring entrepreneurial projects, so the relationship between banks and entrepreneurs is frictionless: entrepreneurs can offer banks perfectly state-contingent securities in exchange for funds. However, banks 1
See also Reinhart and Rogoff (2009); Ball (2014); or Chapter 4 in International Monetary Fund (2009) and references therein.
3
ACCEPTED MANUSCRIPT
face an agency friction in the process of obtaining funds from creditors. As in Gertler and Karadi (2011) and others,2 this friction takes the form of a limited enforcement problem: after borrowing funds, the bank can renege on its debt and divert a certain fraction of resources for its own personal gain, at which point creditors can force it into bankruptcy. The enforcement friction effectively introduces an endogenous constraint on the bank’s lending
CR IP T
to entrepreneurs.
The key determinant of the severity of financial constraints is banks’ net worth. The primary source of fluctuations in aggregate bank net worth is movement in the prices of assets on their balance sheets, which consist of claims on entrepreneurs. Similar to the financial accelerator model by Bernanke, Gertler, and Gilchrist (1999), in general equilibrium there is
AN US
feedback between the price of the equity issued by entrepreneurs and aggregate net worth, which acts as a powerful amplification mechanism: a decline in net worth first forces banks to cut back on project funding. This lowers the price at which new entrepreneurs can sell equity, and at the same time lowers the value of the currently outstanding entrepreneurial securities. In turn, this leads to additional declines in banks’ net worth, further tightening
M
credit to entrepreneurs.
The two features just described—endogenous growth through firm creation and frictions
ED
in financial intermediation—are embedded into a conventional small open economy model allowing for variable capital utilization and for a working capital requirement. These mod-
PT
ifications are standard in the small open economy business cycles literature. Although not critical for the main results on the persistent effects of financial crises, they help enhance
CE
the quantitative properties of the model at little cost of added complexity. The model’s main quantitative application consists in exploring its ability to account
AC
for the South Korean experience during the East Asian financial crisis. The motivation for focusing on Korea is threefold. First, Korea in the 1990s was a highly innovative economy— its ratio of R&D to GDP was among the highest in the world—and so the mechanism linking innovation to TFP growth was likely relevant.3 Second, there is broad agreement in the 2
See also Gertler and Kiyotaki (2010) or Gertler, Kiyotaki, and Queralto (2012). The ratio of R&D expenditures to GDP in Korea was 2.25% in 1996, close to that in the U.S. (2.44%) or Switzerland (2.45%) and above that of France, Germany and other European economies. Source: World Bank WDI. 3
4
ACCEPTED MANUSCRIPT
literature on the crisis trigger (namely, a sudden outflow of capital), facilitating the modeling of a crisis experiment, and also on the importance of financial stresses in exacerbating the crisis, consistent with the model. Third, the length of time elapsed since the crisis permits a clear assessment of the extent to which the crisis led to permanent losses in TFP and GDP, which the data suggest is large.
CR IP T
In the model, the crisis is triggered by an increase in the country risk premium, capturing the sudden stop that occurred in Korea in late 1997. The Korean evidence is used to estimate the model’s key parameters. In addition to a standard set of macroeconomic variables, I use two additional series to discipline the model’s key channels: business-sector expenditures on R&D, providing evidence on innovation, and a measure of private-sector credit spreads, a
AN US
proxy for the severity of financial disruption. The model can account well for the Korean data, particularly regarding the medium-run behavior following the crisis. Endogenous TFP growth via innovation is key for the model to generate the observed high persistence: it allows a crisis shock to induce a slowdown in the growth rate of TFP, leading to lasting effects on the real economy. Financial frictions play a quantitatively large role in generating
M
this persistence—with output, TFP, and other variables falling by much less when frictionless financial markets are assumed instead.
ED
In addition to the crisis episode, I also examine the model’s performance over the mediumterm cycle more generally, following the approach developed by Comin and Gertler (2006).
PT
I find that the model does a good job of reproducing the features of the Korean data over this dimension as well.
CE
The modeling approach in this paper builds on the literature on financial factors in macroeconomics, pioneered by Bernanke and Gertler (1989) and Kiyotaki and Moore (1997)
AC
and incorporated within a quantitative macroeconomic framework by Bernanke, Gertler, and Gilchrist (1999). The latest vintage of this class of models—which this paper follows most closely—focuses on financial intermediaries, as in Gertler and Karadi (2011). The key difference with this literature is that here financing frictions affect the introduction of innovations, which drives medium-run productivity developments—following the literature on endogenous growth due to Romer (1990). Support for the hypothesis that financing frictions affect innovation is provided in Section 2, which shows that private-sector R&D expenditure 5
ACCEPTED MANUSCRIPT
drops dramatically in recessions associated with financial crises—alongside highly persistent TFP declines—while it falls by much less in recessions that are not associated with financial crises. This paper is also related to a growing literature incorporating endogenous growth mechanisms following Romer (1990) within quantitative macroeconomic frameworks—notably
CR IP T
Comin and Gertler (2006), who propose a model to study medium-term cycles in the United States, and Bilbiie, Ghironi, and Melitz (2012), who analyze producer entry over the business cycle. Comin, Gertler, and Santacreu (2009) estimate a version of the model in Comin and Gertler (2006), and Comin, Loayza, Pasha, and Serven (2014) propose a related framework for developing countries. A very recent set of papers uses frameworks in this spirit to
AN US
analyze the U.S. Great Recession, and U.S. fluctuations more generally. Examples include Anzoategui, Comin, Gertler, and Martinez (2016), Guerron-Quintana and Jinnai (2014), Bianchi and Kung (2014), and Moran and Queralto (2018). This paper differs partly by explicitly modeling financial frictions affecting innovators, and partly by using measures of private-sector R&D expenditures and credit spreads in the quantitative analysis.4
M
The evidence and model presented here are related to Aguiar and Gopinath (2007), who argue that a distinctive feature of emerging market business cycles is large movements
ED
in trend TFP growth. The evidence in this paper suggests that financial crises are one instance in which such non-stationary behavior occurs. In addition, the model introduced
PT
below provides a foundation for the type of TFP process taken as exogenous by Aguiar and Gopinath (2007) and connects it explicitly with financial market imperfections—thereby
CE
showing how the latter amplify the volatility of the nonstationary component of TFP. This paper also relates to a large literature that proposes quantitative macroeconomic
AC
models for emerging market economies, such as Uribe and Yue (2006) and Neumeyer and Perri (2005). A related branch of this literature has proposed quantitative frameworks to account for financial crises and sudden stops in emerging markets, like Gertler, Gilchrist, and Natalucci (2007), Mendoza (2010) and Mendoza and Yue (2012). Some of these frameworks, as well as several others, offer explanations for the observed decline in TFP during financial 4
Anzoategui, Comin, Gertler, and Martinez (2016) also use private-sector R&D, but do not model financial frictions.
6
ACCEPTED MANUSCRIPT
crises, including declines in capacity utilization, sectoral reallocation, and disruption in trade of imported inputs.5 While these mechanisms and the one introduced in this paper can be seen as complementary, the key difference is that the focus here is on accounting for mediumrun TFP declines, and their role in generating the persistent output effects identified in the data. This focus also differentiates the present paper from other quantitative analyses of the
CR IP T
Korean 1997 crisis, like Gertler, Gilchrist, and Natalucci (2007), Benjamin and Meza (2009), and Otsu (2008).
The model developed in this paper connects with a recent literature linking financial frictions with producer heterogeneity to account for fluctuations in TFP. For example, Sandleris and Wright (2014) and Oberfield (2013) explore how resource misallocation across
AN US
producers can account for the observed movements in TFP following the financial crises in Argentina in 2001 and Chile in 1982, respectively. Motivated by these studies, Buera and Moll (forthcoming) use a canonical model with firm heterogeneity and collateral constraints to explore how a credit crunch may translate into a drop in measured aggregate TFP (and other aggregate “wedges”). Gopinath, Kalemli-Ozcan, Karabarbounis, and Villegas-Sanchez
times in the euro area periphery.
M
(2015) use a model in this class to explain the slow pace of productivity growth in recent
ED
The present paper shares the goal with the literature above of connecting TFP with financial frictions, but focuses on how such frictions affect the extensive margin of firm cre-
PT
ation while abstracting from the intensive margin of reallocation among existing producers. This is done partly for tractability: the resulting model delivers simple aggregation, and can
CE
be integrated within a standard macroeconomic framework in a straightforward way. At the same time, the focus on the extensive margin is consistent with the empirical findings
AC
in Midrigan and Xu (2014), who use firm-level data from emerging economies to document that financial frictions cause much greater losses by distorting firm entry and technology adoption than by inducing misallocation across producers. While Midrigan and Xu (2014) largely focus on how financial frictions may lead to aggregate TFP losses over the long run, the present paper focuses on capturing medium-run dynamics following a financial crisis, and 5
For instance, Gopinath and Neiman (2014), Benjamin and Meza (2009), Meza and Quintin (2007), Kehoe and Ruhl (2009), Pratap and Urrutia (2012), and Aoki, Benigno, and Kiyotaki (2009).
7
ACCEPTED MANUSCRIPT
on developing a tractable model that can capture a broad set of macroeconomic variables. In recent, independent work, Ates and Saffie (2016) build an endogenous growth model with financial frictions to study dynamics following sudden stops. In addition to important differences in the modeling approach (for example, the present framework endogenizes financial constraints as arising from an explicit agency friction) there is a significant difference
CR IP T
in focus between the two papers—Ates and Saffie (2016) emphasize selection effects among entrants, while the present paper’s main focus is on the link between elevated financial stress and persistent TFP losses.
The rest of the paper is organized as follows. Section 2 presents evidence on the behavior of output, productivity, and innovation in financial crises, and briefly describes the Korean
AN US
experience. Section 3 describes the model. Section 4 presents a quantitative analysis of the model, which consists of simple experiments designed to illustrate its workings as well as an application to the Korean data. Section 5 provides concluding remarks.
M
2. Evidence
This section documents highly persistent productivity and TFP losses associated with
ED
financial crises—using a panel of both advanced economies (AEs) and emerging market economies (EMEs)—and it investigates the behavior of business innovation during these events. This is done in subsection 2.1. I review the evidence on the 1997 Korean financial
PT
crisis in subsection 2.2.
CE
2.1. GDP, Productivity and Innovation in Financial Crises The empirical approach follows Cerra and Saxena (2008), who estimate a univariate
AC
autoregressive model for output growth—augmented to include current and lagged dummies indicating financial crises and other shocks—on a panel of countries. I estimate the following model:
xi,t = αi +
J X
β j xi,t−j +
j=1
L X
δ l Di,t−l + εi,t
(1)
l=0
Above, xi,t is the value of the variable of interest in country i and year t, and Di,t is a dummy variable indicating a financial crisis or a recession. As in Cerra and Saxena (2008), 8
ACCEPTED MANUSCRIPT
the specification includes a country fixed effect, αi . The first set of results involves a decomposition of (log) output as the sum of productivity and hours: Yi,t Ni,t
+ log (Ni,t )
Here Yi,t is real GDP, Ni,t is hours worked, and
Yi,t Ni,t
(2)
CR IP T
log (Yi,t ) = log
is productivity. I then estimate
(1) for (first-differenced) log output, productivity and hours separately. Based on the recommendations of AIC and BIC tests as well as the “general-to-specific sequential t rule” (see Chapter 6 in Hayashi (2000)), I set J = 1 and L = 5, although results are robust to
AN US
reasonable variations of the lag structure. The dataset consists of a panel of 36 countries starting in 1950, and includes 50 banking crisis episodes as dated by Laeven and Valencia (2013). All crisis episodes are in the 1970s and later, and they are evenly split between AEs and EMEs.6
The three panels in Figure 1 show the estimated effect of banking crises on the three
M
variables—GDP, hours, and productivity. The decline of output in a banking crisis is very large and persistent, with GDP falling almost 15 percent after five years. Consistent with
ED
the findings in Cerra and Saxena (2008), the GDP losses are never reversed. Turning to the decomposition of GDP, there is highly persistent damage on both productivity and hours.
PT
The key point is that the damage on productivity is larger than on hours: the latter decline by slightly less than 7 percent by the tenth year, while productivity falls 9 percent.
CE
Figure 1 also provides the impulse responses for the AE and EME subsamples separately (the sample contains 25 crisis episodes in AEs and 25 in EMEs). The finding that the output damage is long-lasting holds for both subsamples. Interestingly, the adverse effects for all
AC
three variables are more severe in the AEs—a result that strongly reflects the severity of the 2008 global financial crisis affecting many AEs. In addition, the finding that productivity is at least as important as hours in accounting for the GDP decline also holds for each of the two separate subsamples. Why do productivity and GDP remain persistently depressed following financial crises? 6
Online Appendix E contains the full list of crisis episodes.
9
ACCEPTED MANUSCRIPT
The key channel in the model presented below posits that the tightening of financial constraints that occurs in banking crises leads to a reduction in business innovation, which then causes the persistent slowdown in productivity. To explore this channel empirically, in the next specification I use data on business-sector R&D expenditures as a proxy for innovation.7 I then compare the behavior of R&D, TFP and GDP during recessions associated
CR IP T
with banking crises, with their behavior during other types of recessions. The idea is that banking crises likely feature especially severe financial tightening, compared with recessions not associated with banking crises. In this specification, the sample consists of 34 banking crisis episodes among both AEs and EMEs, and 22 episodes of recessions not associated with banking crises.8
AN US
The first panel in Figure 2 shows that while R&D only falls slightly during a normal recession, banking crises precipitate an enormous drop in R&D, with no evidence of a subsequent turnaround. At the same time, the middle panel shows that TFP follows a pattern very similar to R&D: normal recessions are associated with only a small and transient decline while banking crises feature a large and persistent fall. Similarly, the decline of output in a
M
banking crisis (shown in the last panel) is very large and persistent, with GDP falling more than 10 percent below trend after 5 years; by contrast, output falls only about one third as
ED
much during other recessions.9
Robustness.— The empirical specification (1) assumes that output growth (and the other
PT
response variables considered) can react contemporaneously to a banking crisis, while the occurrence of the crisis is exogenous to current-year output growth. An alternative possi-
CE
bility is that growth is contemporaneously exogenous to the crisis, and the crisis affects it only with a lag. To test for this possibility, I repeat the analysis above setting δ 0 = 0 in
AC
equation (1). The specification is left otherwise unchanged (i.e. same number of lags for 7
I use R&D data because it has the largest coverage in terms of countries and years. Like R&D, the number of new business startups—another proxy for innovation—also shows sharp declines in financial crises. Empirical results are available upon request. 8 I obtain international recession dates from the Economic Cycle Research Institute (ECRI), and then eliminate those ECRI dates that are associated with a banking crisis to create an index of recessions not associated with banking crises (labeled “other recessions”). Appendix E contains the full set of episodes. 9 The behavior of output in Figures 1 and 2 is not exactly the same due to differences in the sample. The sample used to obtain the results in Figure 2 is shorter, a result of the more limited availability of data on business-sector R&D.
10
ACCEPTED MANUSCRIPT
the endogenous variable and for the crisis dummy). As expected, I find that when growth is contemporaneously exogenous to the crisis, the overall responses become somewhat smaller in magnitude. For example, productivity falls about 6 percent after six years, compared to almost 9 percent in Figure 1. The key results, however, remain robust to the alternative specification: all three variables in Figure 1 continue to fall persistently (and remain signif-
CR IP T
icantly below zero with 95 percent confidence), and productivity falls more than hours. As in Figure 2, R&D and TFP continue to fall significantly following banking crises.10
A potential concern with fixed-effects estimation of (1) is inconsistency of the parameter estimates due to the combination of fixed effects and lagged dependent variables (Nickell (1981)). However, because the time series dimension of the data is large, the inconsistency
AN US
problem is likely not a major concern. Appendix A confirms that this is the case by repeating the analysis using the bias correction method by Bruno (2005). The bias-corrected impulse responses are very close to the baseline results, and lie well within the confidence intervals in Figures 1 and 2.11
M
2.2. The 1997 Korean Financial Crisis
This section provides a brief description of the Korean financial crisis of 1997, which is
ED
later used as a quantitative application of the model. The Korean crisis started in the fourth quarter of 1997, following crises in Thailand and other countries in the region. Until then,
PT
the 1990s had been a time of robust growth for the Korean economy. The onset of the crisis was marked by a sudden and massive outflow of capital, mirrored by a sharp rise in the
CE
country risk premium. The capital flight and the ensuing crisis were largely unanticipated.12 The crisis then spilled over to the real economy, which started contracting in the fourth
AC
quarter of 1997 and accelerated its decline in the first quarter of 1998. The blue solid line in Figure 3 shows the behavior of the Korean economy over this period.
In addition to a standard set of macroeconomic variables, the Figure includes business10
These results can be found in the Online Appendix A, Figures (A.1)-(A.2). See Figures (A.3)-(A.4) in Appendix A. 12 According to OECD projections, the Korean economy was projected to continue on its path of robust growth as late as June and even December of 1997 (see Appendix B, Figure B.1). Radelet and Sachs (1998), Krueger and Yoo (2002), and Furman and Stiglitz (1998) also highlight the unexpected nature of the East Asian financial crisis. 11
11
ACCEPTED MANUSCRIPT
sector R&D and TFP (both available only at the annual frequency), as well as a credit spread to proxy for the extent of financial tightness.13 The credit spread is constructed as a weighted average (with equal weights) of two spreads: the spread between Korean 3-year AA- corporate bonds and 3-year Treasury bonds, and the spread between Korean (marginal) bank lending rates and Treasury bonds. The resulting weighted average is likely a better
CR IP T
measure of financial tightness affecting innovators than the simple corporate credit spread, since many firms (particularly startups and small firms) do not have access to the corporate bond market. The equal weights on each spread are chosen to reflect the fact that the value of listed corporate bonds and of aggregate bank loans to the (non-household) non-financial sector around the crisis were roughly equal in magnitude (at a little over 100 trillion won
AN US
each).14
Figure 3 also includes a range of projections based on pre-crisis trends, shown by the blue shaded areas, calculated following the approach in Christiano, Eichenbaum, and Trabandt (2015).15 These projections are computed as follows: for each variable I fit a linear trend from date x to 1997:III, where x ∈ {1985:I, 1995:IV}. For the variables available annually,
M
I use 1997 as the final date and x ∈ {1985, 1995} as the start date. For each x, the linear trend is then extrapolated from 1997:IV onward. The blue shaded areas in Figure 3 represent
ED
the min-max range for the trend projections. The range of projections is a transparent and plausible way to estimate how the economy would have evolved in the years following 1997,
PT
absent the large shocks associated with the financial crisis. The deviations of the actual
13
CE
values with respect to these trends are later used to characterize the impact of the crisis,
AC
Output, consumption, investment, employment and R&D are in real per capita terms. All variables are shown as logs except net exports-to-GDP (NX/Y) and the credit spread, which are shown in levels. 14 Source: Haver Analytics for listed corporate bonds and Bank of Korea for bank loans. 15 Hall (2015) and Blanchard et al. (2015) have recently used similar approaches.
12
ACCEPTED MANUSCRIPT
and to compare the effects of a model-simulated crisis to their empirical counterparts.16,17 As Figure 3 makes clear, the years prior to the crisis were marked by fast GDP and TFP growth (GDP grew at about 6 percent annually on average between 1990 and 1997, and TFP at 4 percent), and remarkably mild fluctuations. The 1997 crisis then led to sharp contractions in the real economy, including TFP, as well as a massive and sudden reversal in
CR IP T
the net exports to GDP ratio. As shown in the bottom left panel, firms’ innovation collapsed, at the same time as credit spreads spiked to unprecedented levels (bottom right panel). The crisis represented a clear “trend break” for the real economy, including (crucially) for TFP: after the crisis all macroeconomic variables remained persistently depressed relative to the range of pre-crisis projections.
AN US
The black line in Figure 5 shows the average deviations in the Korean data after 1997:III, and the gray shaded areas indicate the min-max ranges of these deviations. The gaps between actual and projected values were largely not reversed. Output contracted persistently by almost 15 percent relative to the projection, close to the average permanent output loss in the sample of banking crisis episodes discussed in section 2.1.18 The gap for employment
M
was around 8 percent in the medium run, and TFP fell by about 7 percent relative to the projection. Consumption, and especially investment, declined by much more. Note also
ED
that the drop in consumption was substantially larger than that of output, consistent with the stylized fact that consumption in emerging economies is more volatile than output.19
PT
Mirroring the capital outflow, there was a dramatic reversal in the current account balance, with the ratio of net exports to GDP increasing by more than 15 percent (relative to the
16
CE
projection) in just two quarters. Business-sector R&D fell persistently by more than 30
AC
One possible caveat to the use of this approach is that it may not be plausible to project that the Korean economy would grow indefinitely at an average rate of over 6% (as it did in the 1990s and earlier), even in the absence of the crisis. While it is true that Korean growth eventually converged to levels closer to advanced-economy averages, Figure B.2 in Appendix B shows that in the 5-year period following the crisis trough, average growth was similar to the levels observed prior to the crisis. A second possible objection is that pre-crisis growth in 1990-97 might have been abnormally fast, due to an unsustainable boom fueled by capital inflows. As Table B.1 in Appendix B shows, however, the years before the crisis in which the current account deficit widened did not coincide with an acceleration in growth. 17 A previous version of the paper used ARIMA methods to form pre-crisis projections. Appendix B compares that method to the present one. 18 Cerra and Saxena (2005) find a similar magnitude for the permanent output loss following the Korean crisis, using a different methodology (see their Table 4). 19 See Aguiar and Gopinath (2007), for example.
13
ACCEPTED MANUSCRIPT
percent, and the credit spread rose by 700 basis points. Appendix B includes the behavior of additional proxy measures of innovation and firm creation: the number of business owners per capita (a measure of entrepreneurship, as argued in Stel (2008)), and patent and trademark applications by Korean residents. Like R&D, all three indicators decline sharply in 1998, after rising for almost two decades practically
CR IP T
without interruption. These declines are consistent with the findings in Midrigan and Xu (2014), who find a sharp drop in the number of producers operating during the crisis in the Korean manufacturing sector using an establishment-level dataset covering all manufacturing plants (with more than five workers).20
The evidence thus supports the key mechanism introduced in this paper, namely that a
AN US
financial crisis may lead to a reduction in the pace of innovation, resulting in highly persistent effects of the crisis. Several other authors have documented empirically an adverse effect of financing frictions on firm creation and technology innovation and adoption, both in the U.S. and other OECD countries21 as well as in developing economies.22
In sum, the Korean financial crisis appears to have been triggered by a sudden and
M
unanticipated withdrawal of foreign inflows. This led to a severe tightening of domestic financial conditions, and was associated with sharp losses in the real economy that were
ED
largely not reversed. TFP fell substantially and persistently relative to trend, with evidence suggesting a role for business innovation in accounting for the persistent TFP loss. The
PT
following section introduces a model featuring this mechanism and explore its ability to account for the salient facts of the Korean crisis, with an emphasis on capturing the medium-
20
CE
run declines.
See panel B in Figure 4 of Midrigan and Xu (2014). Kortum and Lerner (2000) establish a positive effect in the U.S. on innovation of the availability of venture capital funding, and Kerr and Nanda (2009) show that U.S. financial reforms enhanced the process of small firm entry. See also Hall (2002) and Hall and Lerner (2009) for surveys of work analyzing the impact of limited access to external finance on innovation and R&D using firm-level data from OECD countries. Ridder (2017) provides evidence that tight financial conditions reduced productivity-enhancing investments in the US during the global financial crisis. 22 For example, Ayyagari et al. (2012) and Gorodnichenko and Schnitzer (2013).
AC
21
14
ACCEPTED MANUSCRIPT
3. Model The core framework is a small open economy with endogenous TFP growth through an expanding variety of intermediates, as in Romer (1990) and Comin and Gertler (2006). The key difference with respect to these frameworks is that there is an imperfection in financial
CR IP T
markets that impedes the smooth flow of resources from savers to banks, who finance the development of innovations. Two additional features are introduced to enhance the model’s quantitative performance: variable capital utilization and a working capital requirement for intermediate goods producers. To avoid additional complexity, the model abstracts from nominal wage and price rigidities and monetary factors—though as in Comin and Gertler
AN US
(2006), it allows for exogenous markup variation as a stand-in for the effects of those frictions. There are four types of agents in the model: households, entrepreneurs, banks, and goods producers. Homogeneous final output is produced using an expanding variety of intermediates. Entrepreneurs use funds borrowed from banks to finance the creation of new varieties. In turn, banks obtain financing from domestic households and from international
M
capital markets. The following sections discuss the behavior of each of these agents.
ED
3.1. Households
The economy is populated by a continuum of measure one of families, and each family h has a unit measure of members. Households make decisions on consumption, labor supply,
PT
investment in physical capital and saving through a risk-free one-period international bond. There are two types of members within each household: workers and bankers, with measures
CE
1 − f and f respectively.23 A fraction of the workers are specialized—or “skilled”—workers who supply labor inelastically to entrepreneurs. Regular workers in family h are monopolistic
AC
suppliers of a differentiated h-specific labor type, used to produce intermediate goods.24 Both types of labor return wages to the family. Bankers manage a financial intermediary that uses borrowed funds to make loans to entrepreneurs. There is perfect consumption insurance among family members. 23
As in Gertler and Karadi (2011), this formulation is a simple way of introducing heterogeneity in terms of borrowers and lenders while maintaining the tractability of a representative agent model. 24 Differentiated labor is introduced to allow for (exogenously) time-varying market power in labor supply, which will induce movement in wage markups.
15
ACCEPTED MANUSCRIPT
There is random turnover between bankers and workers: a banker becomes a worker with probability 1 − σ, at which time he or she transfers accumulated earnings to the family.
f Workers become bankers with probability (1 − σ) 1−f , so there is a measure (1 − σ)f of
new bankers each period—exactly offsetting the number that exit. The family transfers a small amount of resources to new bankers so they are able to start operations. Banker
CR IP T
exit is introduced as a device to ensure that the financial imperfection will remain relevant— otherwise banks might reach a point where internal resources are enough to finance all desired lending.25
Letting Ct denote consumption and Lht hours of work in the intermediates sector, household h’s utility function is 1−ρ
AN US
1 Ct − Γt 1+ L1+ ht U (Ct , Lht ) = 1−ρ
−1
(3)
The preference structure above is common in the emerging market business cycles literature.26 It abstracts from wealth effects on labor supply, as in Greenwood et al. (1988) (GHH
M
henceforth): by assuming that utility is a function of the excess of consumption relative to the disutility of labor, the marginal rate of substitution between consumption and labor
ED
becomes independent of consumption. The specification ensures that increases in country interest rates do not lead to counterfactual booms in employment and output via positive
PT
movements in labor supply.27
The term multiplying the disutility of work, Γt , is assumed to depend on the aggregate
CE
technological level, At , as follows: Γt = Aγt Γ1−γ t−1
(4)
AC
where γ ∈ (0, 1). The emerging market business cycle literature typically features preferences with Γt = 1 for all t. In these frameworks, however, there is usually no long-run growth. In the present framework with endogenous productivity growth, constant Γt would imply trend growth in hours worked. The presence of Γt ensures a balanced growth path with 25
Exogenous exit can also be interpreted as a way of motivating dividend payouts by banks. For example, Mendoza and Yue (2012), Mendoza (2010), or Uribe and Yue (2006). 27 See, for example, the discussion in Neumeyer and Perri (2005). 26
16
ACCEPTED MANUSCRIPT
constant hours. At the same time, so long as γ is small, fluctuations in At will have a small impact on labor supply at medium frequencies, consistent with the usual formulation of GHH preferences.28,29 A large number of competitive “employment agencies” combine specialized labor into a
Lt =
Z
1
0
1/µ Lht wt dh
µwt
CR IP T
homogeneous labor input used by intermediate goods producers, according to (5)
The variable governing the elasticity of substitution across labor types, µwt , follows a firstorder autoregressive process: µ ˆ wt = ρµ µ ˆ wt−1 + µt , where hats denote log-deviations from mization, demand for labor variety h is
Lht = R
1 0
1/(1−µwt )
Wht
1−µwt dh .
Wht Wt
µwt − 1−µ
wt
Lt
(6)
M
where Wt =
AN US
steady state, and with µt an iid random variable. From employment agencies’ cost mini-
Household h’s problem is to choose stochastic sequences for consumption Ct , labor supply
ED
to the intermediates sector Lht , debt Dt , following-period capital Kt+1 , net investment Itn ,
28
CE
PT
and capital utilization ut to solve the following problem:30 max
{Ct+i ,Lht+i ,Dt+i , ∞ n ,u Kt+i+1 ,It+i t+i }
i=0
Et
∞ X
β i U (Ct+i , Lht+i )
(7)
i=0
Jaimovich and Rebelo (2009) propose a specification with similar properties. Under the interpretation of GHH preferences as a reduced form for an economy with home or nonmarket production, as in Benhabib et al. (1991), a small value of γ might be viewed as capturing a process of “slow diffusion” of the varieties used in the business sector into the home sector. As long as γ is positive, home productivity would still grow at the same rate as At at low frequencies, precluding households from perpetually supplying more labor to the business sector. 30 Because all households h will end up making the same decisions on consumption, investment, utilization, and capital and bond holdings, the subindex h from these variables is omitted at the onset.
AC
29
17
ACCEPTED MANUSCRIPT
subject to Ct + Itn + δ(ut )Kt + Rt−1 Dt−1 ≤ rtk ut Kt + Wht Lht + WSt LS + Dt + τ t Kt+1 ≤ Kt + Υ(Itn /Kt )Kt
(8) (9)
CR IP T
and (6) for all t. Here, δ(ut ) denotes the capital depreciation rate, Rt−1 is the gross interest on the non-contingent international bond from t − 1 to t, rtk is the rental rate on capital services, Wht is the wage rate for labor type h, WSt is the skilled labor wage, LS is the supply of skilled labor, and τ t denotes net transfers from the financial intermediation sector.
In addition to accumulating physical capital, households set its utilization rate:31 they
AN US
convert their stock of capital Kt into capital services ut Kt , which are rented out to intermediates firms at rental rate rtk . The capital depreciation rate δ(ut ) is an increasing and convex function of the utilization rate:
a 1+δ ut 1+δ
(10)
M
δ(ut ) = ˜δ +
The household’s uses of funds (the left hand side of (8)) include consumption, net investment,
ED
repair of depreciated capital, and debt repayments. The household’s sources of funds (the right hand side of (8)) are earnings from renting capital services, labor income, new debt
PT
issues, and net transfers from bankers.
Households combine units of the final output Itn with existing capital Kt to produce new
CE
capital goods, using the constant returns to scale production function Υ(Itn /Kt )Kt . The function Υ(Itn /Kt ) is increasing, concave, and assumed to satisfy Υ (in ) = in and Υ0 (in ) = 1, where in is the net investment-capital ratio along the balanced growth path. The concavity
AC
assumption introduces costs of adjusting the level of the capital stock. As made clear by (8)(9), repair of depreciated capital does not entail adjustment costs, an assumption commonly made in the literature that ensures that the capital utilization decision does not depend on the (shadow) price of capital. 31
I assume households make capital utilization and accumulation decisions for convenience. At the cost of additional notation, it is possible to use a decentralization in which those decisions are made by firms that yields identical equilibrium conditions.
18
ACCEPTED MANUSCRIPT
The country interest rate Rt is the product of an exogenous world interest rate, R∗ , and a country borrowing premium Ψt : Rt = R∗ Ψt . The premium is assumed to depend on total domestic debt held by foreign investors—denoted Bt —relative to trend, and on a stochastic Bt S −d Ψt = ert + ψ e At − 1
(11)
CR IP T
disturbance rtS :
As usual in the small open economy literature, the reason for introducing a positive dependence of the cost of borrowing on foreign indebtedness is to ensure stationary dynamics around the trend. The parameter ψ is set to a very small value so that this feature does not affect the medium-run dynamics of the model. The random disturbance rtS is assumed to
AN US
S + rt , where rt is an iid innovation. follow a first-order autoregressive process: rtS = ρR rt−1
A positive innovation to rtS , interpretable as a rise in the country risk premium, is a simple way to model the sudden capital outflows that initiated the Korean crisis. Each household h sets the wage according to
M
µwt Γt Lht = Wht
ED
The variable µwt represents a markup of the wage over the household’s marginal rate of substitution between labor and consumption. Note that each household will charge the same wage and supply the same amount of labor, a condition that is imposed henceforth.
PT
Let UCt denote marginal utility of consumption and Λt,t+i the household’s stochastic
AC
CE
discount factor, given by the equations βUCt+i UCt −ρ 1 1+ = Ct − Γt L 1+ t
Λt,t+i =
(12)
UCt
(13)
and let PKt be the shadow price of capital, i.e. the Lagrange multiplier on constraint (9) divided by UCt . Then the household’s first-order conditions for Dt , Kt+1 , Itn , and ut are the
following: 1 = Et (Λt,t+1 ) Rt
(14) 19
ACCEPTED MANUSCRIPT
(
k ut+1 − δ(ut+1 ) + PKt+1 rt+1 1 = Et Λt,t+1 PKt " #−1 Itn PKt = Υ0 Kt
)
rtk = δ 0 (ut )
(15) (16)
CR IP T
(17)
where equation (15) omits terms that are of second order.32
Equations (14) and (15) are two conventional bond and capital Euler equations. Equation (16) implies that the shadow price of capital PKt is an increasing function of the ratio Itn /Kt . Equation (17) sets the marginal depreciation rate equal to the capital rental rate,
AN US
the marginal benefit of utilization. 3.2. Entrepreneurs
A representative competitive entrepreneur uses skilled labor and final output as inputs to produce new product varieties, or innovations. An innovation is interpreted as the full design
M
of a distinct product variety, including all the instructions needed to manufacture it. The model makes no distinction between entirely novel products or possible adaptations made
ED
by entrepreneurs in the small open economy of products already in use in more advanced countries. The assumption is that adapting to the local economy an innovation from a
PT
more developed country still requires costly development efforts.33 As described in section 3.4, final output is produced by combining the existing varieties of intermediates. The entrepreneurial sector can be interpreted as capturing innovation broadly defined, including
CE
business start-ups.34
Let At be the total number of firm varieties in operation in period t, and Zt the number of
AC
new varieties created in period t. Both existing and newly created varieties face the risk of an 32
n h n i o n I It+1 k The optimality condition is 1 = Et Λt,t+1 rt+1 ut+1 −δ(ut+1 )+PKt+1 +PKt+1 Υ Kt+1 − /P . Kt K t+1 t+1
Given the assumptions on Υ(·), which ensure there are no adjustment costs along the balanced growth path, In
In
the term PKt+1 Υ Kt+1 − Kt+1 is zero along the balance growth path and to a first order around it. t+1 t+1 33 Mansfield et al. (1981) provide evidence that imitation costs are substantial. 34 An alternative, equivalent formulation of the entrepreneurial sector would consist of an unbounded mass of potential entrants, each endowed with one idea for a potential new variety, which can be developed by paying a sunk entry cost given by the marginal cost associated with the innovation production function described below.
20
ACCEPTED MANUSCRIPT
exogenous exit (or obsolescence) shock: any variety becomes obsolete with probability 1 − φ. The exit shock occurs at the end of the period, after production and entry, so proportion 1 − φ of new innovations will never be active.35 Accordingly, the evolution of the aggregate stock of innovations, At , is given by: (18)
CR IP T
At+1 = φ (At + Zt )
The entrepreneur operates a Cobb-Douglas production function that transforms skilled labor, LSt , and final output, Nt , into innovations Zt :
(19)
AN US
Zt = Ntη (At LSt )1−η
As in Romer (1990), (19) incorporates a positive externality from the aggregate technological level, At , on the efficiency of skilled labor in developing innovations. This assumption is key to generate endogenous growth. The total input cost of the entrepreneur, interpreted as
M
R&D expenditure, is Rt ≡ Nt + WSt LSt , where WSt is the skilled labor wage. Once the entrepreneur has created an innovation, he or she is granted the sole ownership
ED
of the new product design. The entrepreneur then rents the design to monopolists who manufacture the new good, for a rental rate π t each period. In equilibrium, π t will equal the profits of monopolist producers, as described in section 3.4. Thus, an innovation developed
PT
in period t is an asset which provides the entrepreneur a payoff stream given by the random sequence {π t+i }∞ i=1 , so long as the innovation does not become obsolete.
CE
The entrepreneur has no internal funds at the beginning of period t. To cover R&D costs, he or she obtains financing from banks. There are no contracting frictions between
AC
entrepreneurs and banks. The representative entrepreneur obtains funds in period t by selling Zt securities—one for each innovation created—in a market in which banks are the buyers. These securities pay π t+i in each future period t + i > t if the underlying innovation is still in
operation (and zero if the variety has become obsolete). The price of one of these securities is denoted Jt . 35
The timing assumption follows Bilbiie et al. (2012).
21
ACCEPTED MANUSCRIPT
The entrepreneur thus makes zero profits, state-by-state, in all future periods. In period t, he or she obtains revenues Jt Zt from the sales of securities, and pays the input cost Nt + WSt LSt . The entrepreneur’s problem in period t is Jt Zt − Nt − WSt LSt
subject to (19). It follows that (see Appendix F for details) η 1−η 1 WS,t /At Jt = η 1−η
(20)
CR IP T
max
Zt ,Nt ,LSt
(21)
AN US
The right hand side of (21) is the marginal cost of innovation. Equation (21) states that this marginal cost equals the price of entrepreneurs’ equity, Jt . Equity sales exactly cover R&D expenditures, and the entrepreneur makes zero profits in period t: Jt Zt = Nt + WSt LSt . The amount of final output used in innovation is given by
(22)
M
Nt = ηJt Zt
ED
Skilled-labor market clearing implies LSt = LS . As shown in Appendix F, combining this condition with (21) and the first-order condition for LSt from (20) yields a simple and intuitive relation between the price of entrepreneur equity Jt and the number of new varieties
CE
PT
Zt :
1 Jt = η
1 Zt LS At
1−η η
(23)
AC
Equation (23) can be interpreted as an aggregate supply curve of innovations, with aggregate product creation relative to the stock of innovations
Zt At
an increasing function of the price
of equity Jt . 3.3. Banks
Financial intermediaries (“banks,” for short) lend funds obtained in capital markets to entrepreneurs. Banks are interpreted as specialists who assist in channeling funds from savers to entrepreneurs. They are meant to capture the entire financial intermediation sector, i.e. 22
ACCEPTED MANUSCRIPT
investment and savings institutions as well as traditional commercial banks.36 Banks’ assets consist of securities issued by entrepreneurs, and their liabilities include short-term debt and net worth. There is an agency friction between banks and their creditors which takes the form of a limited enforcement problem, modeled as in Gertler and Karadi (2011) and Gertler and Kiyotaki (2010): at the end of the period, after borrowing funds, a
CR IP T
bank can default on his or her debt and divert an exogenous fraction θ of resources. This imposes a limit on how much leverage banks are able to take on ex-ante. The optimization problem of a continuing banker j is the following: Et
σ
i−1
i=1
h i (1 − σ)Λt,t+i φ (π t+i + Jt+i ) Sjt+i−1 − Rt+i−1 Djt+i−1
AN US
max
{Sjt+i−1 ,Djt+i−1 }∞ i=1
(∞ X
subject to
Jt Sjt + Rt−1 Djt−1 ≤ φ (π t + Jt ) Sjt−1 + Djt
i=1
σ i−1 (1 − σ)Λt,t+i [φ (π t+i + Jt+i ) Sjt+i−1 − Rt+i−1 Djt+i−1 ] ≥ θJt Sjt
(24)
(25)
(26)
M
Et
∞ X
)
From the budget constraint (25), the bank’s use of funds (left hand side) includes the pur-
ED
chase of Sjt claims on entrepreneurs, which cost Jt Sjt , as well as debt repayments, Rt−1 Djt−1 . The sources of funds are the revenues obtained from securities purchased the previous period
PT
(the first term on the right hand side) as well as new debt issued, Djt . Of the securities purchased last period, Sjt−1 , fraction 1 − φ receive the obsolescence shock. The remainder
CE
generate payoff π t for the bank and can be sold on the market at price Jt . As indicated by (24), if a bank alive at t exits in subsequent period t + i (which occurs
AC
with probability σ i−1 (1 − σ)), it transfers the accumulated wealth back to the household. The net resources accumulated until period t + i are given by the term in square brackets in
(24). The bank values payoffs in the period and state at which it exits using the stochastic
36 Investment institutions and other “shadow” financial intermediaries played an increasingly important role in the Korean financial sector in general, and in lending to small firms in particular, in the years prior to the 1997 crisis. For example, securities dealers—a subset of these investment institutions—promoted the establishment of the KOSDAQ equity market in 1996, as a means of financing venture businesses and small firms. See Bali˜ no and Ubide (1999) for a description of the Korean financial sector before the crisis.
23
ACCEPTED MANUSCRIPT
discount factor Λt,t+i . Equation (26) is the bank’s incentive constraint. At the end of period t, after having borrowed in the debt market, the bank may choose to default on its creditors and divert fraction θ of available funds, which it transfers back to the household. Creditors can then force the bank into bankruptcy and recover the remaining fraction 1 − θ of resources, but it
CR IP T
is too costly for them to recover the fraction θ of funds that the bank diverted. Accordingly, for creditors to be willing to supply funds to the bank, equation (26) must hold: the bank’s value if it honors the contract with his creditors must be greater than the value of diverting resources in the amount θJt Sjt and being shut down.
Bank j’s net worth, denoted Wjt ≡ Jt Sjt −Djt , is the critical state variable in the banker’s
AN US
problem. From equation (25), it evolves according to the following law of motion: Wjt = (RZt − Rt−1 ) Jt−1 Sjt−1 + Rt−1 Wjt−1
(27)
M
where RZt is the rate of return to one dollar invested in entrepreneurial claims: φ (π t + Jt ) Jt−1
(28)
ED
RZt ≡
As shown in Appendix G, when the constraint binds the bank’s asset holdings Jt Sjt are
Jt Sjt = Φt Wjt
(29)
CE
PT
constrained by its net worth Wjt , as follows:
where Φt is the maximum leverage ratio allowed by the incentive constraint, which is deter-
AC
mined recursively by the following set of equations: νt θ − µt ν t = Et Λt,t+1 1 − σ + σ(µt+1 Φt+1 + ν t+1 ) Rt µt = Et Λt,t+1 1 − σ + σ(µt+1 Φt+1 + ν t+1 ) (RZt+1 − Rt )
Φt =
(30) (31) (32)
Here, ν t is the expected discounted value of a unit of net worth, and µt is the expected 24
ACCEPTED MANUSCRIPT
discounted excess return on banks’ lending relative to the deposit rate. Note that the leverage ratio varies only with the aggregate state (and not with bank-specific variables) and depends positively on ν t and µt , and inversely on the parameter θ governing the severity of agency frictions.37 Entering bankers in period t receive a small transfer from households so they can start ξ f
of the value of assets in
CR IP T
operations. The transfer is assumed to be an exogenous fraction
the previous period, so that if bank j is a new entrant its net worth is given by Wjt =
ξ Jt−1 St−1 f
(33)
AN US
The problem of new entrants is thus identical to the one described above, but replacing the budget constraint (25) with Jt Sjt ≤ fξ Jt−1 St−1 + Djt .38
Aggregation.— Let St denote the aggregate quantity of securities and Wt aggregate bank Rf Rf net worth: St = 0 Sjt dj, Wt = 0 Wjt dj. Aggregating (29) across banks yields
M
Jt St = Φt Wt
(34)
ED
Aggregating Wjt across all banks (continuing ones and new entrants) and using (27) and (33) yields
PT
Wt = σ [(RZ,t − Rt−1 ) Jt−1 St−1 + Rt−1 Wt−1 ] + (1 − σ)ξJt−1 St−1
(35)
CE
The key source of fluctuations in aggregate bank net worth is movement in RZt , which arises from movements in the price Jt and in profits π t .
AC
Market clearing for entrepreneurs’ securities implies that the aggregate claims held by banks must equal the total number of existing varieties in period t, including those newly 37
Appendix G contains a detailed derivation of the banker’s problem. It is important that fξ > 0, so that newborn bankers have some initial equity capital. Otherwise, in light of (29), they would not be able to make any loans in their initial period. 38
25
ACCEPTED MANUSCRIPT
created during t: St = At + Zt . Therefore, equation (34) becomes Jt (At + Zt ) = Φt Wt
(36)
These considerations clarify how financial factors affect the evolution of TFP. When net
CR IP T
worth is low, through equation (36) the aggregate number of projects that the intermediation sector can finance is reduced, making “demand” for new innovations Zt lower. With a smaller Zt the growth rate of TFP will decline, as indicated by (18).
Frictionless financial markets.—Suppose there are no enforcement frictions, so that banks
AN US
are unconstrained. Perfect arbitrage then implies Et (Λt,t+1 RZ,t+1 ) = Et (Λt,t+1 )Rt = 1, or Jt = φEt [Λt,t+1 (π t+1 + Jt+1 )]
(37)
Without financial frictions, entry occurs until the value of entrepreneur equity, Jt , equals profits plus the price of equity in the following period, adjusted by the household’s discount
M
factor and accounting for obsolescence. In the absence of speculative bubbles, iterating (37) forward yields an expression for the equity price as the present discounted value of the
ED
expected stream of profits.
With financial frictions and constrained banks, we will have Et {Λt,t+1 (RZt+1 − Rt )} > 0,
PT
or equivalently
Jt < φEt [Λt,t+1 (π t+1 + Jt+1 )]
(38)
CE
The gap between the left- and right-hand side of (38) widens whenever banks’ financial constraints are tighter, which happens in times in when net worth of the intermediation
AC
sector is low. This implies that the rate of new firm creation falls below its frictionless level. The imperfection in financial markets also implies that the growth rate of TFP, and therefore of output, along the balanced growth path is below its value in a model without financial frictions.39 39
See Levine (1997), Levine (2005) for surveys of evidence suggesting that financial development has a positive impact on long-run TFP and output growth.
26
ACCEPTED MANUSCRIPT
3.4. Final Output and Intermediates Producers In each period t there exist a continuum of varieties of intermediate goods of measure At , indexed by i ∈ [0, At ]. The final good is produced by a competitive sector that uses the Z
Yt =
At
ϑ−1 ϑ
Yit
0
di
ϑ ϑ−1
CR IP T
different varieties of intermediates as inputs, by means of the production function
(39)
here Yt is the amount of final output produced, and Yit is the amount of intermediate i used in production. The representative final output producer chooses input quantities Yit RA for i ∈ [0, At ] by minimizing input costs 0 t Pit Yit di subject to producing a given level of
AN US
final output, where Pit is the price of variety i. Solving this problem yields a demand curve for each intermediate variety i:
Yit = where the price level Pt is
Z
Pit Pt
At
−ϑ
Yt
(40)
1 1−ϑ
(41)
Pit1−ϑ di
M
Pt =
0
ED
Each intermediate variety is produced by a competitive monopolist that employs a Cobb-
PT
Douglas technology with capital services Kit and labor Lit as inputs: Yit = Kitα L1−α it
(42)
CE
Intermediate goods firms face a working capital requirement that forces them to hold a quantity of non-interest-bearing assets at least as large as multiple θW of the quarterly wage
AC
bill:
κit ≥ θW Wt Lit
(43)
with θW ≥ 0, where κit denotes the amount of working capital held by firm i in period t. As shown in Uribe and Yue (2006) and Mendoza and Yue (2012), this formulation implies that the effective cost of labor is [1 + θW (Rt − 1)/Rt ] Wt (reflecting the opportunity cost of holding non-interest-bearing assets), and therefore an increase in the interest rate Rt raises 27
ACCEPTED MANUSCRIPT
the effective cost of labor. Working capital is assumed to be issued by a domestic financial intermediary (distinct from the bankers that fund entrepreneurs) which rebates profits to households as a lump-sum. The objective of producer i is to maximize profits π it : max
Pit ,Yit ,Kit ,Lit
Pit Yit − [1 + θW (Rt − 1)/Rt ] Wt Lit − rtk Kit Pt
(44)
CR IP T
π it =
subject to (40) and (42). The solution of this problem is standard, and details are relegated to Appendix H. All firms i set prices to a markup ϑ/(ϑ − 1) over marginal cost of production. Thus each firm i sets the same price and sells the same quantity, and therefore profits are
AN US
the same for all i. The Appendix shows how the following simple expression for profits in terms of aggregate output and technology can be obtained: πt =
1 Yt ϑ At
(45)
M
where the subindex i has been omitted from profits in light of the previous remarks. The expression above gives the payoff to entrepreneurs from renting a novel product design, via
ED
competition among potential producers of the new variety. As shown in Appendix H, combining (39) and (42) yields the aggregate production func-
1
Yt = Atϑ−1 (ut Kt )α L1−α t
(46)
CE
PT
tion
AC
RA where aggregate labor and capital services, Lt and ut Kt are given by Lt = 0 t Lit di and RA ut Kt = 0 t Kit di. Firms’ optimality conditions give rise to the following two equations: Yt ϑ = (1 − α) ϑ−1 Lt ϑ Y t rtk =α ϑ−1 ut Kt
[1 + θW (Rt − 1)/Rt ] Wt
(47) (48)
where use has been made of the aggregate production function (46). Marginal products of capital and labor are set to a markup ϑ/(ϑ − 1) over their respective marginal costs. 28
ACCEPTED MANUSCRIPT
3.5. Equilibrium and Aggregate Resource Constraint ∞ An equilibrium in this economy is defined as an exogenous stochastic sequence µwt , rtS t=0
and stochastic sequences for the eleven quantity variables { Ct , Γt , Lt , Bt , Kt+1 , Itn , ut , Zt ,
∞ At+1 , Nt , Wt }∞ t=0 , the four banking sector coefficients {Φt , µt , ν t , RZt }t=0 , and the seven ∞ prices Rt , Jt , PKt , rtk , Wt , WSt , π t t=0 such that: (i) The state variables {Γt , Kt+1 , At+1 , Wt }∞ t=0
CR IP T
follow the laws of motion given by (4), (9) with equality, (18), and (35); (ii) Households,
entrepreneurs, bankers, final output producers, and intermediates producers solve their optimization problems; (iii) Markets for entrepreneurs’ securities, skilled labor, regular labor, capital services, and final output clear.
By combining the budget constraints of bankers and households and using market-clearing
AN US
conditions, the following aggregate resource constraint can be obtained (see Appendix I for a derivation):
Bt − Rt−1 Bt−1 + Yt = Ct + Itn + δ(ut )Kt + Nt
(49)
M
where Bt is equals the sum of household and banking sector debt: Bt = Dt + Dt , with Dt ≡ Rf Djt dj. Equation (49) indicates that the economy uses output and international borrowing 0
ED
to finance consumption, investment in physical capital, and investment in innovation.
Given At , Kt , Γt−1 , Wt−1 , the twenty-two endogenous variables listed above are deter-
PT
mined by equations (4), (9), (11), (14)-(17), (18), (21)-(23), (28), (30)-(32), (35), (36), (45), (46)-(48), and (49). The economy features long-run growth in aggregate TFP, as well as in
CE
output and its components. The model is solved by first detrending appropriately to obtain a stationary system, and then computing a log-linear approximation around the steady
AC
state of that stationary system. Appendix J provides a list of the complete set of equations determining equilibrium and details on the detrended system. 4. Model Analysis This section presents numerical results from dynamic simulations of the model. The goal is to illustrate how the novel mechanism introduced in the paper—namely, financially constrained innovation—is able to produce the persistent effects of financial crises reported 29
ACCEPTED MANUSCRIPT
in Section 2. Section 4.1 describes the model’s parameterization and estimation. I use a limited information approach similar to Christiano et al. (2005) and Uribe and Yue (2006) to estimate key model parameters. The approach consists in choosing the parameters to minimize the distance between the effects of the Korean crisis estimated in section 2.2 and the analogous
CR IP T
objects in the model. In section 4.2, I perform simple experiments in the parameterized model designed to illustrate its workings. Section 4.3 discusses the model’s fit to the Korean crisis, and analyzes the role played by each of the model’s key mechanisms (financial frictions and endogenous innovation) in accounting for the Korean experience. Section 4.4 examines
4.1. Parameter Values and Estimation
AN US
the model’s performance over the business cycle more generally.
There are 19 parameters in the model, not including parameters related to the shock processes. I divide these parameters into two sets. The first set contains eleven standard parameters which I calibrate to conventional values. The second set contains eight parameters
M
which are estimated. I describe each set of parameter values in turn. Calibrated parameters.— This set consists of nine standard preference and technology
ED
parameters (β, , ρ, α, ϑ, θW , ˜δ, a, ψ, d), and also includes the transfer rate to starting bankers (ξ). Table C.1 in the Appendix shows their assigned values as well as a reminder of their
PT
meaning. I fix the quarterly discount factor, β, at 0.99, as in Gertler et al. (2007). Also following Gertler et al. (2007), I assume the wage elasticity of labor supply is equal to 2,
CE
and accordingly set = 1/2. This choice for the curvature of utility in labor is similar to the values typically used in studies of business cycles in small open economies, such as
AC
Mendoza (1991), Neumeyer and Perri (2005), Uribe and Yue (2006), and Mendoza and Yue (2012). The inverse of the intertemporal elasticity of substitution, given by ρ, is set at 2, also
a standard value in the literature (e.g., Mendoza (1991), Uribe and Yue (2006), Mendoza (2010)). I set the capital share α = 0.37 following Elekdag et al. (2006), who fit a small
open economy DSGE model to Korean data. The parameter governing the elasticity of final output with respect to intermediates, ϑ, is chosen so that the technological level At takes the purely labor-augmenting form, which from equation (46) amounts to imposing the restriction
30
ACCEPTED MANUSCRIPT
(1 − α)(ϑ − 1) = 1.40 This restriction implies that there exists a balanced growth path along which output is proportional to TFP, and therefore profits per period π t are stationary (see equation (45)). Given the choice for α, the resulting value for the intermediate goods markup is ϑ/(ϑ − 1) = 1.63, close to the value of 1.6 chosen by Comin and Gertler (2006). similar to the value estimated by Uribe and Yue (2006).
CR IP T
The working capital parameter θW is set to unity, following Neumeyer and Perri (2005) and Parameter ˜δ in equation (10) is set to ensure that depreciation is 2.5 percent each quarter along the balanced growth path, and adjust a so that capital utilization along the balanced growth path is normalized to unity. I set the elasticity of the country risk premium to aggregate foreign debt, ψ, to 10−5 . This value ensures that the dynamics of the model are
AN US
virtually unaffected by the debt-elastic risk premium at high and medium frequencies, while still making the foreign asset position revert to trend over the very long run. As usual in the literature, I assume the (net) country risk premium is nil along the balanced growth path, which implies that the foreign asset position relative to trend, B/A, is pinned down by the parameter d. I calibrate this parameter to match an average net exports to GDP
M
ratio of 1 percent, the Korean average in the decade prior to the crisis. The resulting value is d = 2.54. This value implies a ratio of B to Y of about 0.8 along the balanced growth
ED
path (an annual debt ratio of 20 percent). Finally, I set the new banker transfer rate to a very small value, ξ = 10−5 , which ensures that this parameter has no bearing on any of the
PT
model’s properties.41
Shock processes.— The first-order autoregressive coefficient ρR is set to 0.92, a value
CE
obtained by fitting an AR(1) process to quarterly data for the Korean country interest rate.42
AC
The standard deviation of the innovation rt is set to 0.20/100 so that the model matches the 40
Kung and Schmid (forthcoming) make a similar parameter restriction. As explained earlier, the purpose of a positive transfer rate ξ is to allow for some initial equity capital for entering bankers so that they are able to make loans in their initial period; otherwise, they would not be able to lend in the period they enter, which would preclude them from accumulating equity for subsequent periods. I verified that reducing ξ to even lower values has no effect on the results reported. 42 The estimated value for this autoregressive coefficient is sensitive to restricting the sample to the precrisis period (which yields about 0.86) as opposed to using the full sample, including the post-crisis (which gives 0.97). Because using only the pre-crisis sample likely understates the perceived persistence of the shock once it hit in late 1997, and because the pre-crisis sample is quite short, I set the autoregressive coefficient to the average across the two estimates. 41
31
ACCEPTED MANUSCRIPT
high-frequency component of fluctuations in the Korean country interest rate (see Section 4.4.) The value of the autoregressive parameter of the markup shock is set to ρµ = 0.88, following Comin and Gertler (2006). The standard deviation of the markup innovation is 0.76/100—a value chosen so that the model matches the high-frequency volatility of Korean GDP, as explained in Section 4.4.
CR IP T
Estimated parameters.— This set consists of eight parameters. Of these, three are preference and technology parameters for which there are no readily available estimates, or for which there is no clear consensus in the literature. These include: the parameter governing the impact of the total number of varieties At on labor disutility (γ), the elasticity of the price of capital to net investment (denoted ϕ),43 and the elasticity of marginal deprecia-
AN US
tion with respect to the utilization rate (δ). The remaining five parameters relate to the entrepreneurial and financial sectors. They include the survival rate of innovations (φ), the aggregate supply of skilled labor (LS ), the materials share in innovation (η), the banker survival rate (σ), and the divertable fraction of bank assets (θ).
To estimate this parameters, I first simulate a “crisis experiment” within the model,
M
meant to capture the Korean crisis. The disturbance that initiates the crisis is a shock to the country borrowing premium, Ψt , of 500 basis points, as in Gertler et al. (2007). The idea
ED
is to capture the fact that what precipitated the onset of the Korean crisis was a sudden and unanticipated outflow of capital, which is treated as exogenous.44 Because the shock
PT
in Korea occurred between late 1997:IV and early 1998:I, I model the increase in Ψt as two consecutive and equally sized innovations to rtS which lead to a total increase of 500 basis
CE
points in Ψt . As shown in Figure D.1, the model interest rate replicates the run-up in the Korean country risk premium well.
AC
Given the exogenous driving force, I then estimate the model’s parameters by minimizing the distance between the impulse responses in the model and the gaps between the actual In
The elasticity of PKt with respect to Ktt is ϕ ≡ −Υ00 (in ) in (see equation (16)). Given the assumptions on Υ(·) and the solution procedure used, no other features of Υ(·) need to be specified. 44 This is consistent with the view of Radelet and Sachs (1998), who argue that the crisis was triggered by a financial panic among international investors. On the other hand, Burnside et al. (2001) argue that the outflow reflected “prospective” fiscal deficits resulting from bailing out the banking sector. In my analysis, banking sector vulnerability plays an important role, but I continue to treat the increase in the country premium as exogenous. 43
32
ACCEPTED MANUSCRIPT
evolution of the Korean data post-1997:III and the projections calculated in Section 2.2. Let the subset of estimated model parameters be ε ≡ (γ, ϕ, δ, φ, LS , η, σ, θ), and let Ξ(ε) denote the mapping from ε to the model’s impulse responses to the country risk shock initiating b be the average gaps between actual and projected values. I use five the crisis. Let also Ξ b includes twenty elements years of data starting in 1997:IV (1998 for R&D and TFP), so Ξ
CR IP T
for each of the six quarterly variables and five elements for the two annual variables. The model counterpart of the credit spread in the data is given by the (expected) yield on loans to entrepreneurs relative to the riskless rate. It is computed assuming a maturity of three years (consistent with the maturity of the corporate bonds used in constructing the empirical credit spread) and expressed in annual terms.45
AN US
Then ε is estimated by solving
h i0 h i −1 b b min Ξ − Ξ(ε) V Ξ − Ξ(ε) ε
(50)
Here, V denotes a diagonal matrix with the min-max ranges of the estimated gaps along the
M
main diagonal. The weighting matrix V gives relative more weight to more precise estimates in the optimization problem. To avoid underweighting the annual variables relative to the
ED
quarterly ones, I multiply their weights times four. Table C.2 summarizes the resulting parameter estimates. The elasticity of the labor
PT
disutility term to aggregate technology is estimated to γ = 0.096—a small value, indicating that preferences are close to the GHH formulation and reflecting the sustained decline in
CE
labor input in the aftermath of the crisis. The investment adjustment cost parameter ϕ is estimated at 0.026, a value at the low end of the range reported by Bernanke et al.
AC
(1999), and in line with results in Uribe and Yue (2006). The latter authors estimate the parameter equivalent to the inverse of ϕ to be 72.8, implying a somewhat lower value of
ϕ than I find. The value for the elasticity of capital depreciation, δ, is very low, implying
a high responsiveness of utilization. This reflects the model’s attempt to generate the fast decline in measured TFP observed in the data. As shown below (see Figure 6), variable 45
Thus, the model credit spread is calculated as Et
nP 11
33
o [log(R ) − log(R )] /3 . Z,t+1+j t+j j=0
ACCEPTED MANUSCRIPT
utilization helps the model account for the near-term movement in measured TFP, with endogenous innovation accounting for the medium-run TFP decline. The range of values for this parameter found in the literature is quite wide, with Christiano et al. (2005) finding a very low value as I do.46 Turning to the parameters relating to the innovation sector, I estimate the variety survival
CR IP T
rate to be φ = 0.86, and the skilled labor supply to be LS = 0.15. These parameters help determine the time pattern of the TFP decline. The relatively high death rate of innovations partly reflects the fast decline in TFP observed during the Korean crisis. The estimated ratio of skilled labor supply to regular labor is about one sixth. The estimate for η is 0.19, suggesting a low materials intensity in innovation, and thus a high weight of labor in
AN US
the production of innovations—higher than in the production of intermediate goods. The resulting value of the ratio of materials used in innovation to output (N/Y ) is 6.8%, relatively close to the ratio of investment in intangibles to output for Korean manufacturing of 4.5% found by Midrigan and Xu (2014).
Turning to the financial sector parameters, I find that the estimation procedure drives
M
the banker survival rate σ to very low values. This reflects in part the very large, but relatively short-lived, increase in the Korean spread, which the model can generate via a low
ED
value of σ.47 To avoid implausibly low values of σ I set a lower bound for this parameter, equal to 0.9375 (implying an expected horizon of only four years, or equivalently an annual
PT
dividend payout of 4(1 − σ) = 25 percent of net worth). Once the bound is imposed, the estimation drives σ to its lower bound. The point estimate for the divertable fraction of
CE
assets is θ = 0.26. This value is within the range found in related studies—for example, Gertler et al. (2016) or Gertler and Kiyotaki (2015). Given these parameters, the steady-
AC
state credit spread equals 100 basis points, a value in the vicinity of the Korean spread in non-crisis times. 46
The point estimate for this parameter found by Basu and Kimball (1997) is unity, although the estimate is quite imprecise. 47 The credit spread in the model moves inversely with financial sector net worth. Low σ implies a high steady-state leverage ratio as well as low persistence of net worth. The former tends to make the spread more volatile, while the latter decreases its persistence.
34
ACCEPTED MANUSCRIPT
4.2. Simple Experiments I begin with three simple experiments designed to illustrate the behavior of the model. Figure 4 shows the model responses to three disturbances: a rise in the country premium Ψt (first row), a rise in the markup µwt (second row), and a shock to financial intermediaries’ net worth (third row). The figure shows the response of output Yt , along with three key variables
CR IP T
relating to the innovation and financial sectors: the aggregate number of firm varieties At , the R&D expenditure Rt , and the credit spread facing entrepreneurs. The last column shows the evolution of the corresponding forcing variable. In each case, the blue solid line shows the responses of the baseline model, and the green dashed line shows the behavior of the model without financial frictions (as described at the end of section 3.3 and in Appendix
AN US
J.2).
The rise in the country premium is a one-standard-deviation innovation in rtS (see equation (11)), leading the country interest rate to rise by 0.7 percent per year. From the blue solid line in the first panel, note that the shock has highly persistent effects on output,
M
which remains permanently below the unshocked path of the economy. Behind the behavior of output is the response of the aggregate number of varieties At , which drops persistently
ED
as a result of the sharp decline in R&D: intuitively, a reduction in the pace of innovation leads to a temporary decline in the growth rate of At , implying a permanent decline in its level.
PT
How does the model’s transmission mechanism operate? The initiating disturbance to Ψt leads to a decline in the value of entrepreneur equity Jt , as the stream of expected future
CE
profits per new intermediate good becomes lower and more heavily discounted. This has the effect of generating a large drop in the net worth of the intermediation sector, as equation
AC
(35) indicates. The shock leads the equity price Jt to fall much more in the baseline model than in the model without financial frictions, leading to a sharp rise in the credit spread. This is at the core of the model’s amplification mechanism: as banks’ constraints tighten— reflected in skyrocketing credit spreads—they are forced to cut back on project funding to a larger extent than would be the case with frictionless financial markets. Along the way, there is adverse feedback effect between net worth and the firm value Jt : as the former falls, banks’ constraints tighten, forcing a decline in the credit available for innovators. The decrease in 35
ACCEPTED MANUSCRIPT
demand for new projects leads to further drops in Jt , starting a new round of declines in bank net worth. The end result is a large decline in R&D expenditure—as made clear by the panel in the third column—which drops much more sharply in the baseline model than in the frictionless case. Overall, financial frictions play a quantitatively large role in leading to larger medium-run declines: aggregate technology At and output Yt fall about seventy
CR IP T
and fifty percent more, respectively, after five years in the baseline model compared to the frictionless model.
The second experiment is an increase in the markup µwt , resulting from a positive innovation to this variable of one standard deviation. As seen in the last column, the markup rises by 0.76 percent and then slowly reverts back to steady state. The rise in markups initiates
AN US
a decline in labor input and therefore in output, which drops about 1.6 percent after four quarters. As a consequence of the fall in activity, the profits from owning an innovation decline, setting off a drop in the asset price Jt which reduces intermediaries’ net worth and triggers the financial accelerator. Financial conditions tighten, with the credit spread rising almost sixty basis points. Technology At falls about 0.8 percent after 15 years—resulting
M
from the decline in R&D—which accounts for the fall in output over the same horizon. Note, however, that now output bounces back to some extent, relative to its short-run drop: the
ED
reason is that labor input recovers as the effects of the markup shock unwind. Still, the amount of medium-run damage is substantial, with output eventually recovering only about
PT
half the initial decline (0.8 versus 1.6 percent). By contrast, in the frictionless model the magnitude of the bounceback is much larger, about two thirds (1.5 percent drop initially,
CE
compared with 0.5 percent after 15 years). As explained above, at the core of the model’s transmission mechanism is the behavior
AC
of financial sector net worth. To illustrate this point, the third row of Figure 4 analyzes a transfer of wealth (e.g. via taxation) from financial intermediaries to households. In particular, I consider a one-time reduction of intermediary capital of one-third of its steady state value. As seen in the last column, this leads to a much larger decline in net worth on impact, due to the general-equilibrium drop in the asset price Jt . In addition, given σ > 0 the decline in net worth is persistent even though the transfer is purely transient, as equation (35) makes clear. The decline in net worth tightens intermediaries’ incentive constraints, 36
ACCEPTED MANUSCRIPT
which impedes their access to credit and leads to a rise in spreads. This then reduces the amount of financing available for entrepreneurs, leading to persistent losses in TFP and output. In the model with no financial frictions, by contrast, the shock has no aggregate consequences, as it simply represents a redistribution of wealth within the representative
CR IP T
household. 4.3. Accounting for the Korean Crisis
I now turn to comparing the theoretical impulse responses with the empirical gaps computed in section 2.2. Figure 5 shows the evolution of the model’s variables in response to the initiating shock (relative to the balanced growth path) along with their empirical coun-
AN US
terparts, from 1997 through the end of 2002.48 Overall, the model (blue circled line) does a good job of reproducing the empirical behavior of all variables following the crisis (shown in black). In particular, the model is able to capture the persistent gaps in the real variables observed in the data. The crucial element that allows the model to capture these mediumrun effects is the persistent decline in the aggregate number of firm varieties, At , in response
M
to the shock. This decline helps the model match the behavior of Korean measured TFP. At the same time, it depresses labor demand, leading to the persistent drop in employment. The
ED
behavior of the latter in the model also tracks its empirical counterpart well. The slowdown in aggregate technology At , along with the medium-run decline in inputs, then accounts for
PT
the persistent drop in output. In addition, the model also does a good job at capturing the empirical gaps of investment, consumption, and the trade balance. Most importantly,
CE
the model reproduces the sharp decline in business-sector R&D and the spike in the credit spread following the crisis—both at the core of the model’s transmission mechanism.
AC
Next, I compare the crisis experiment in the baseline model with the responses to the same shock in two alternative model versions: the frictionless model discussed at the end of section 3.3, and another version in which the endogenous growth mechanism is absent altogether. The results are included in Figure 5, with the responses in the frictionless model shown in green dashed, and the responses in the “exogenous growth” model shown in red dotted. 48
Figure D.2 in Appendix D compares model and data over a longer horizon.
37
ACCEPTED MANUSCRIPT
Financial frictions play an important role in amplifying the impact of the crisis shock. Without financial market frictions the credit spread is fixed at zero (as seen in the bottomright panel), and as a result the decline in R&D is much smaller. As a consequence, the real variables fall by much less: for example, the decline in output relative to trend four years after the shock is about 9 percent in the case without financial frictions, compared to 14
CR IP T
percent in the baseline model and in the data. The degree of amplification is even larger for TFP: by 2002 this variable is about 6 percent below trend in the baseline model (similar to the data), but is depressed by only 1.8 percent in the frictionless model. As explained in the preceding section, these differences are driven by the endogenous amplification implied by the interaction between the financial intermediation sector and the process of firm creation.
AN US
Figure 5 also reports the responses in a model in which the endogenous growth channel is inactive (red dash-dotted line)—what is then left is a standard RBC model of a small open economy with variable capital utilization and a working capital requirement, subjected to an interest rate shock. As made clear by the Figure, the overall declines in this case are much smaller in magnitude, and very far from the data—especially regarding the persistence of
M
the gaps—due to the absence of endogenous movements in At .49
Figure 6 provides more information on the behavior of TFP in the baseline model. Mea-
ED
sured TFP in the model can be decomposed into the contribution of the number of varieties 1
in operation and the contribution of capital utilization, as follows: TFPt = Atϑ−1 uαt . The
PT
figure shows the overall drop in measured TFP (the black circled line), along with the contribution of varieties (the blue bars) and the contribution of capital utilization (the red bars).
CE
Observe that the relative importance of the technology-driven component grows with the time elapsed since the shock: in 1998, the drop in measured TFP is largely accounted for by
AC
a drop in utilization, with technology explaining about one third of the overall decline. In contrast, by 2002 most of the weakness in TFP is accounted for by the technology component. 49
In fact, measured TFP in the exogenous growth model actually increases after two years, due to an increase in utilization. The reason is the following: utilization can be expressed as a monotonic increasing function of the L/K ratio. In the exogenous growth version of the model, employment starts recovering immediately after the shock hits, while the capital stock falls sluggishly—implying that the response of the L/K ratio eventually turns positive.
38
ACCEPTED MANUSCRIPT
4.4. Business Cycle Simulations This section examines the model’s performance over the business cycle. To this end, the Korean data is grouped into two sets: a set of real variables consisting of annual observations on GDP, consumption, investment, the net exports-to-GDP ratio, employment, businesssector R&D, and business-sector TFP, for the period 1975 through 2017; and a set of financial
CR IP T
variables consisting of quarterly series on the Korean credit spread from 1987:I through 2017:IV, and for the Korean country interest rate from 1994:I through 2017:IV. Appendix E provides details on data construction. As in Comin and Gertler (2006), I apply the bandpass filter (Christiano and Fitzgerald (2003)) to the real variables to extract medium-term fluctuations, where the medium term cycle is defined as including frequencies between 0 and
AN US
50 years. Figure D.3 in Appendix D displays the filtered data.50 The shorter sample size of the financial data complicates the task of extracting the medium-term cycle for these series, so I focus on the high-frequency component of these variables.51
Table 1 shows the standard deviations of the medium-term cycle, along with the high-
M
frequency (0 to 8 years) and the medium-frequency (8 to 50 years) components. Two features of the data are worth noting. First, the medium-term cycle is highly volatile: it features
ED
across-the-board higher standard deviations than the high frequency component, including (notably) for TFP. This is explained by the volatility of the medium-frequency component, which in most cases is at least twice as volatile as the high-frequency component (with the
PT
net exports ratio being one exception). Second, the usual volatility ranking across variables at the high frequency is preserved in the medium-term cycle: consumption is somewhat more
CE
volatile than output over the medium term, and investment much more so—between two and three times as much.
AC
Turning to the cyclical behavior of the real variables, consumption, investment, employ-
ment and particularly TFP are all strongly correlated with output over the medium term 50
I also experimented with filtering quarterly GDP, consumption, investment and NX/Y, which are available at the quarterly frequency (and starting in the 1960s). I found that the main statistics from the quarterly series are close to those calculated at the annual frequency, particularly regarding the medium-term cycle. Comin and Gertler (2006) reach a similar conclusion on the US data. 51 Applying the medium-term filter to the available financial series, I have found that these variables display much larger fluctuations at the high frequency relative to the medium frequency than is the case for the real variables.
39
ACCEPTED MANUSCRIPT
(Figure 7). Interestingly, R&D exhibits a significant lead over GDP over the medium-term cycle. It also displays a strong lead over TFP (see Figure D.4 in Appendix D), with a time pattern very similar to that with GDP. This fact is consistent with the evidence documented by Moran and Queralto (2018) for the US and for a set of advanced economies, who use VAR methods to show that increases in R&D tend to lead to increase gradual, persistent rises in
CR IP T
TFP.
Table 2 and Figure 8 document the standard deviations and cross-correlations with GDP of the financial variables. In each case, moments refer the quarterly high-frequency components. Two observations stand out: first, both financial series display considerable volatility at the high frequency, with a quarterly standard deviation of nearly 100 basis points in each
AN US
case. Second, both variables exhibit strong negative correlation with GDP. The country interest rate shows a strong negative comovement with (quarterly) output contemporaneously, while elevated levels of the credit spread lead adverse developments in GDP by several quarters (Figure 8).
Next, I turn to evaluating the behavior of the baseline model against the Korean data.
M
By construction, the model exactly matches the high-frequency standard deviations of the country interest rate and of GDP.52 The question is then how well the model can reproduce
ED
the volatility observed in the Korean data (particularly of the medium-term cycle), and whether it matches the cyclical comovement with output of the different variables.
PT
The model does a reasonably good job of replicating the standard deviations of the medium-term cycle. The model-based standard deviations are well within the confidence in-
CE
tervals for three variables (GDP, consumption, and investment), and just marginally outside for NX/Y and for TFP. On the other hand, the model underpredicts the volatility of R&D
AC
over the medium-term cycle by a substantial amount, while it gets closer to its volatility at the high frequency. One possibility here is that the evolution of Korean R&D may reflect “one-off” factors, as the country went from very low ratios of R&D to GDP in the 1970s to much higher ones in the 1990s and 2000s. Indeed, the standard deviation of medium-term R&D for the post-1990 period falls to about 16.9 percent, while the other variables’ standard 52
Country interest rate shocks explain about sixty percent of the high-frequency volatility of GDP, with markup shocks accounting for the remainder.
40
ACCEPTED MANUSCRIPT
deviations remain relatively unchanged when computed post-1990. The model also does quite well at capturing the medium-term cyclical comovements in the data. For most variables, the model cross-correlations lie mainly within the confidence variables. One variable for which the model is somewhat at odds with the data is the net exports-to-GDP ratio: the model predicts negative comovement between lagged NX/Y and
CR IP T
GDP, while in the data this variable does not exhibit strong cyclicality in either direction (the confidence intervals include zero throughout). Still, the contemporaneous correlation is similar in the model and in the data, at a small negative value. Countercyclical NX/Y in the model reflects the important role of country interest rate shocks in driving fluctuations. One relevant consideration here is that the volatility of the country interest rate is calibrated to
AN US
the post-1994 period, which may overstate the importance of interest rate shocks over the entire sample. A second aspect in which the model does less well is in matching the lead of R&D over GDP in the data, which the model captures to an extent (up to about 5 years, i.e. k = −5 in Figure 7) but not fully. One way to address this within the model is to add adjustment costs to R&D expenditure, as Moran and Queralto (2018) do.
M
Finally, the model does well at matching the standard deviations and correlations with GDP of the financial variables. In particular, it does a very good job of reproducing the
ED
high-frequency cross-correlation of the credit spread with GDP (Figure 8). Appendix D includes an additional experiment examining the model’s ability to reproduce
PT
the evolution of the Korean economy during the 2009 recession. The model can account for the 2009 episode quite well. The analysis reinforces the point that financial factors are crucial
CE
in generating a slow recovery: absent increases in the country interest risk premium and with financial frictions turned off, output bounces back much more from its initial decline, and
AC
the medium-run loss in TFP becomes significantly smaller. 5. Conclusion This paper has sought to account for the phenomenon of highly persistent output losses observed following financial crises. It has argued that such a phenomenon emerges naturally in a model in which innovation endogenously sustains trend productivity growth, and in which financial imperfections work to impede the smooth flow of resources to fund innovators’ 41
ACCEPTED MANUSCRIPT
investments. Thanks to these mechanisms, the model accounts well for the Korean experience following the 1997–98 financial crisis, as well as for the short- and medium-run fluctuations in the Korean data more generally. While the model’s main quantitative application is based on the Korean 1997 crisis, the 2008 wave of financial crises in advanced economies was also accompanied by extremely
CR IP T
persistent drops in activity, and productivity growth in these countries slowed significantly compared to previous averages.53 In several countries, including the US and the euro area, the crisis coincided with a period in which monetary policy was constrained by the zero bound. An interesting area for future research would be to explore how the mechanisms introduced in this paper might be used to address the experience of the advanced economies,
AN US
as well as the implications of the zero lower bound on policy rates in the type of environment analyzed here.54
Another potentially interesting application of the framework presented in this paper would be an evaluation of the welfare gains of government financial policy. Gertler and Kiyotaki (2010), Gertler et al. (2012) and Akinci and Queralto (2014), for example, analyze
M
different government interventions aimed at mitigating the adverse effects of financial crises, finding important benefits of intervention. The endogenous productivity growth mechanism
ED
introduced in this paper would likely affect what is at stake when considering intervention during a financial meltdown, and it could therefore have a substantial impact on the welfare
AC
CE
PT
gains of government policies directed at ameliorating the impact of a crisis.
53
See, for instance, the July 2017 Monetary Policy Report to Congress, p. 12-13. This avenue has been pursued in recent work by Benigno and Fornaro (2015) and by Moran and Queralto (2018). 54
42
ACCEPTED MANUSCRIPT
References Aguiar, M., Gopinath, G., 2007. Emerging market business cycles: The cycle is the trend. Journal of Political Economy 115 (1), 69–102. 1, 19
Finance Discussion Paper (1121). 5
CR IP T
Akinci, O., Queralto, A., 2014. Banks, capital flows and financial crises. FRB International
Anzoategui, D., Comin, D., Gertler, M., Martinez, J., 2016. Endogenous technology adoption and r&d as sources of business cycle persistence. 1, 4
Aoki, K., Benigno, G., Kiyotaki, N., 2009. Capital flows and asset prices. In: NBER In-
AN US
ternational Seminar on Macroeconomics 2007. University of Chicago Press, pp. 175–216. 5
Ates, S., Saffie, F., 2016. Fewer but better: Sudden stops, firm entry, and financial selection. 1
M
Ayyagari, M., Demirg¨ u¸c-Kunt, A., Maksimovic, V., 2012. Firm innovation in emerging markets: the role of finance, governance, and competition. Journal of Financial and Quanti-
ED
tative Analysis 46 (06), 1545–1580. 22 Bali˜ no, T., Ubide, A., 1999. The korean financial crisis of 1997—a strategy of financial sector
PT
reform. IMF Working Paper. 36
CE
Ball, L. M., 2014. Long-term damage from the great recession in oecd countries. National Bureau of Economic Research. 1
AC
Basu, S., Kimball, M. S., 1997. Cyclical productivity with unobserved input variation. 46 Benhabib, J., Rogerson, R., Wright, R., 1991. Homework in Macroeconomics: Household Production and Aggregate Fluctuations. Journal of Political Economy, 1166–1187. 29
Benigno, G., Fornaro, L., 2015. Stagnation traps. 54 Benjamin, D., Meza, F., 2009. Total factor productivity and labor reallocation: the case of the korean 1997 crisis. The B.E. Journal of Macroeconomics 9 (1). 1, 5 43
ACCEPTED MANUSCRIPT
Bernanke, B., Gertler, M., 1989. Agency costs, net worth, and business fluctuations. American Economic Review, 14–31. 1 Bernanke, B., Gertler, M., Gilchrist, S., 1999. The financial accelerator in a quantitative business cycle framework. Handbook of Macroeconomics 1, 1341–1393. 1, 4.1
CR IP T
Bianchi, F., Kung, H., 2014. Growth, slowdowns, and recoveries. National Bureau of Economic Research. 1
Bilbiie, F., Ghironi, F., Melitz, M., 2012. Endogenous entry, product variety and business cycles. Journal of Political Economy 120 (2), 304–345. 1, 35
AN US
Blanchard, O., Cerutti, E., Summers, L., 2015. Inflation and activity–two explorations and their monetary policy implications. Tech. rep., National Bureau of Economic Research. 15 Bruno, G. S., 2005. Approximating the bias of the lsdv estimator for dynamic unbalanced panel data models. Economics letters 87 (3), 361–366. 2.1
M
Buera, F. J., Moll, B., forthcoming. Aggregate implications of a credit crunch: The impor-
ED
tance of heterogeneity. American Economic Journal: Macroeconomics. 1 Burnside, C., Eichenbaum, M., Rebelo, S., 2001. Prospective deficits and the asian currency
PT
crisis. Journal of Political Economy 109 (6), 1155–1197. 44 Cerra, V., Saxena, S. C., 2005. Did output recover from the asian crisis? IMF Staff Papers,
CE
1–23. 18
Cerra, V., Saxena, S. C., 2008. Growth dynamics: the myth of economic recovery. American
AC
Economic Review 98 (1), 439–457. 1, 2.1, 2.1, 2.1
Christiano, L., Eichenbaum, M., Trabandt, M., 2015. Understanding the great recession. American Economic Journal: Macroeconomics 7 (1), 110–67. 2.2
Christiano, L. J., Eichenbaum, M., Evans, C. L., 2005. Nominal rigidities and the dynamic effects of a shock to monetary policy. Journal of Political Economy 113 (1), 1–45. 4, 4.1
44
ACCEPTED MANUSCRIPT
Christiano, L. J., Fitzgerald, T. J., 2003. The band pass filter. international economic review 44 (2), 435–465. 4.4 Comin, D., Gertler, M., 2006. Medium-term business cycles. American Economic Review, 523–551. 1, 3, 4.1, 4.4, 50
sources of output and asset price fluctuations. 1
CR IP T
Comin, D., Gertler, M., Santacreu, A. M., 2009. Technology innovation and diffusion as
Comin, D. A., Loayza, N., Pasha, F., Serven, L., 2014. Medium term business cycles in developing countries. Tech. rep. 1
AN US
Elekdag, S., Justiniano, A., Tchakarov, I., 2006. An estimated small open economy model of the financial accelerator. IMF Staff Papers, 219–241. 4.1
Furman, J., Stiglitz, J. E., 1998. Economic crises: Evidence and insights from east asia. Brookings Papers on Economic Activity, 1–135. 12
M
Gertler, M., Gilchrist, S., Natalucci, F. M., 2007. External constraints on monetary policy and the financial accelerator. Journal of Money, Credit and Banking 39 (2-3), 295–330. 1,
ED
4.1
Gertler, M., Karadi, P., 2011. A model of unconventional monetary policy. Journal of Mon-
PT
etary Economics 58, 17–34. 1, 23, 3.3
CE
Gertler, M., Kiyotaki, N., 2010. Financial intermediation and credit policy in business cycle analysis. Handbook of Monetary Economics 3 (3), 547–599. 2, 3.3, 5
AC
Gertler, M., Kiyotaki, N., 2015. Banking, liquidity, and bank runs in an infinite horizon economy. American Economic Review 105 (7), 2011–43. 4.1
Gertler, M., Kiyotaki, N., Prestipino, A., January 2016. Wholesale banking and bank runs in macroeconomic modelling of financial crises (21892). 4.1 Gertler, M., Kiyotaki, N., Queralto, A., 2012. Financial crises, bank risk exposure and government financial policy. Journal of Monetary Economics 59, 17–34. 2, 5 45
ACCEPTED MANUSCRIPT
Gopinath, G., Kalemli-Ozcan, S., Karabarbounis, L., Villegas-Sanchez, C., 2015. Capital allocation and productivity in south europe. National Bureau of Economic Research. 1 Gopinath, G., Neiman, B., 2014. Trade adjustment and productivity in large crises. American Economic Review 104 (3), 793–831. 5
CR IP T
Gorodnichenko, Y., Schnitzer, M., 2013. Financial constraints and innovation: Why poor countries don’t catch up. Journal of the European Economic Association 11 (5), 1115– 1152. 22
Greenwood, J., Hercowitz, Z., Huffman, G. W., June 1988. Investment, capacity utilization,
AN US
and the real business cycle. American Economic Review 78 (3), 402–17.
URL http://ideas.repec.org/a/aea/aecrev/v78y1988i3p402-17.html 3.1 Guerron-Quintana, P., Jinnai, R., 2014. Liquidity, trends, and the great recession. 1 Hall, B. H., 2002. The financing of research and development. Oxford review of economic
M
policy 18 (1), 35–51. 21
Hall, B. H., Lerner, J., 2009. The financing of r&d and innovation. Tech. rep., National
ED
Bureau of Economic Research. 21
Hall, R. E., 2015. Quantifying the lasting harm to the us economy from the financial crisis.
PT
NBER Macroeconomics Annual 29 (1), 71–128. 15
CE
Hayashi, F., 2000. Econometrics. Princeton University Press. 2.1 International Monetary Fund, 2009. World Economic Outlook, October 2009 Edition. 1
AC
Jaimovich, N., Rebelo, S., 2009. Can news about the future drive the business cycle? The American Economic Review 99 (4), 1097–1118. 28
Kehoe, T. J., Ruhl, K. J., 2009. Sudden stops, sectoral reallocations, and the real exchange rate. Journal of Development Economics 89 (2), 235–249. 5 Kerr, W. R., Nanda, R., 2009. Democratizing entry: Banking deregulations, financing constraints, and entrepreneurship. Journal of Financial Economics 94 (1), 124–149. 21 46
ACCEPTED MANUSCRIPT
Kiyotaki, N., Moore, J., 1997. Credit cycles. Journal of Political Economy 105 (2), 211–248. 1 Kortum, S., Lerner, J., 2000. Assessing the contribution of venture capital to innovation. RAND Journal of Economics, 674–692. 21
CR IP T
Krueger, A. O., Yoo, J., 2002. Chaebol capitalism and the currency-financial crisis in korea. In: NBER Conference on Preventing Currency Crises in Emerging Markets. University of Chicago Press, pp. 601–662. 12
Kung, H., Schmid, L., forthcoming. Innovation, growth, and asset prices. Journal of Finance.
AN US
40
Laeven, L., Valencia, F., 2013. Systemic banking crises database. IMF Economic Review 61 (2), 225–270. 2.1
Levine, R., 1997. Financial development and economic growth: Views and agenda. Journal
M
of Economic Literature 35 (2), 688–726. 39
Levine, R., 2005. Finance and growth: theory and evidence. Handbook of economic growth
ED
1, 865–934. 39
Mansfield, E., Schwartz, M., Wagner, S., 1981. Imitation costs and patents: an empirical
PT
study. The Economic Journal, 907–918. 33
CE
Mendoza, E. G., 1991. Real business cycles in a small open economy. The American Economic Review, 797–818. 4.1
AC
Mendoza, E. G., 2010. Sudden stops, financial crises, and leverage. American Economic Review 100 (5), 1941–1966. 1, 26, 4.1
Mendoza, E. G., Yue, V. Z., 2012. A general equilibrium model of sovereign default and business cycles. The Quarterly Journal of Economics 127 (2), 889–946. 1, 26, 3.4, 4.1 Meza, F., Quintin, E., 2007. Factor utilization and the real impact of financial crises. The B.E. Journal of Macroeconomics 7 (1). 5 47
ACCEPTED MANUSCRIPT
Midrigan, V., Xu, D. Y., 2014. Finance and misallocation: Evidence from plant-level data. American Economic Review 104 (2), 422–58. 1, 2.2, 20, 4.1 Moran, P., Queralto, A., 2018. Innovation, productivity, and monetary policy. Journal of Monetary Economics 93, 24–41. 1, 4.4, 54
CR IP T
Neumeyer, P. A., Perri, F., 2005. Business cycles in emerging economies: the role of interest rates. Journal of Monetary Economics 52 (2), 345–380. 1, 27, 4.1
Nickell, S., 1981. Biases in dynamic models with fixed effects. Econometrica: Journal of the Econometric Society, 1417–1426. 2.1
AN US
Oberfield, E., 2013. Productivity and misallocation during a crisis: Evidence from the chilean crisis of 1982. Review of Economic Dynamics 16 (1), 100–119. 1
Otsu, K., 2008. A neoclassical analysis of the korean crisis. Review of Economic Dynamics 11 (2), 449–471. 1
M
Pratap, S., Urrutia, C., 2012. Financial frictions and total factor productivity: Accounting
ED
for the real effects of financial crises. Review of Economic Dynamics 15 (3), 336–358. 5 Radelet, S., Sachs, J., 1998. The onset of the east asian financial crisis. Tech. rep., National
PT
Bureau of Economic Research. 12, 44 Reinhart, C. M., Reinhart, V. R., 2010. After the fall. National Bureau of Economic Research.
CE
1
Reinhart, C. M., Rogoff, K., 2009. This Time is Different: Eight Centuries of Financial Folly.
AC
Princeton University Press. 1
Ridder, M. d., 2017. Investment in productivity and the long-run effect of financial crisis on output. 21
Romer, P., 1990. Endogenous Technological Change. Journal of Political Economy, S71–S102. 1, 3, 3.2
48
ACCEPTED MANUSCRIPT
Sandleris, G., Wright, M. L., 2014. The costs of financial crises: Resource misallocation, productivity, and welfare in the 2001 argentine crisis. The Scandinavian Journal of Economics 116 (1), 87–127. 1 Stel, A., 2008. The compendia data base: Comparative entrepreneurship data for interna-
CR IP T
tional analysis. In: Measuring Entrepreneurship. Springer, pp. 65–84. 2.2 Uribe, M., Yue, V. Z., 2006. Country spreads and emerging countries: Who drives whom?
AC
CE
PT
ED
M
AN US
Journal of International Economics 69 (1), 6–36. 1, 26, 3.4, 4, 4.1, 4.1
49
CR IP T
ACCEPTED MANUSCRIPT
PT
ED
M
AN US
Figure 1: Impulse Responses to a Banking Crisis
AC
CE
Note: The figure reports impulse responses of output, hours, and productivity to a banking crisis, obtained by estimating equation (1). The black solid line represents the full sample, the orange dashed line restricts the sample to emerging market crises, and the blue dash-dotted line restricts the sample to advanced economy crises. Dark and light shaded regions indicate 1 standard deviation and 95 percent confidence intervals respectively, computed by drawing 100,000 realizations of the coefficients in equation (1) from a multivariate normal with covariance given by the estimated coefficient covariance matrix, and computing the impulse responses for each set of coefficients.
50
CR IP T
ACCEPTED MANUSCRIPT
PT
ED
M
AN US
Figure 2: Impulse Responses to Banking Crises and Other Recessions
AC
CE
Note: The figure reports impulse responses of business-sector R&D expenditures, TFP, and output to banking crises (blue solid line) and to recessions not associated with banking crises (red dashed line, labelled “Other Recessions”). Impulse responses are obtained by estimating (1). Shaded regions indicate 1 standard deviation confidence intervals calculated as described in note to Figure 1.
51
CE
PT
ED
M
52
AN US
Figure 3: The Korean Financial Crisis
CR IP T
ACCEPTED MANUSCRIPT
AC
Note: The figure reports Korean variables (blue solid line) around the 1997 crisis, along with the range of projections based on linear extrapolated pre-crisis trends (light blue shade). Grey vertical shaded areas indicate recession dates.
CR IP T
ACCEPTED MANUSCRIPT
Figure 4: Simple Experiments A
0
R&D
0 -0.01
1
-0.01
% p.a.
-0.01
log
log
Ψ
log
-0.02 -0.03
0.5
-0.04 -0.015
0
-0.05 -0.02
0
20
40
-0.02
60
GDP
0
0
20
40
-0.06
60
A
0
0
-0.01
40
60
GDP
0
0
-0.004
-0.004
60
% p.a.
0
20
40
60
R&D
20
-0.01
CE
0
40
quarters
60
0
20
40
log
60
-0.02
-0.04
0
60
0
20
40 quarters
Baseline
60
40
Markup
0.004 0.002
0
20
40
0
60
Credit Spread
0
20
40
60
Net Worth
0
-0.2
-0.4
-0.6
0
20
40 quarters
60
-0.8
0
20
40 quarters
No financial frictions
Note: The figure shows impulse responses to country premium (top row), wage markup (middle row), and net worth (bottom row) shocks, for the baseline model (blue solid) and for the model without financial frictions (green dashed).
AC
60
0.006
0.5
-0.5
20
0.008
0
quarters
0
0.01
1
-0.03
-0.008
40
Credit Spread
1.5
-0.006
-0.008 -0.01
-0.2
-0.01
log
log
-0.006
-0.04
0
PT
-0.002
40
A
0
-0.002
20
20
0.2
0
% p.a.
20
0
0.6
-0.03
ED
0
-0.015
-0.02
M
log
log
log
µw
-0.02
Net worth
53
-0.01 -0.015
-0.5
60
0.4
-0.005 -0.01
40
R&D
0
-0.005
20
0.4
0.2
AN US
-0.015
0.6
% p.a.
-0.005
Country Interest Rate
0.8
log
-0.005
Credit Spread
1.5
log
GDP
0
60
CR IP T
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
54
AN US
Figure 5: Korean Crisis, Model v. Data
Note: The figure shows the data average deviations from pre-crisis trends (black solid) and the min-max range deviations (grey shaded area), along with the simulation corresponding to the baseline model (blue solid line with circles), the model without financial frictions (green dashed line), and the model with exogenous growth (red dotted line).
CR IP T
ACCEPTED MANUSCRIPT
Figure 6: Baseline Model, TFP Decomposition
AN US
0 -0.01 -0.02 -0.03
M
-0.04
-0.06 -0.07
Model: Measured TFP
Contribution of Varieties: A 1/(ϑ-1) Contribution of Utilization: u α
PT
-0.08
ED
-0.05
1997
1998
1999
2000
2001
2002
AC
CE
Note: The figure reports model measured TFP in the crisis experiment (black circled line) along with the contribution of technology (blue bars) and of capital utilization (red bars).
55
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
Figure 7: Cross-Correlation with GDP, Medium-Term Cycle
Note: Each panel shows the medium-term-cycle correlation between GDP at time t and the corresponding variable at time t − k in the data (black dotted line, 95-percent confidence bands indicated by the shaded area) and in the baseline model (blue circled line). Data confidence intervals obtained using the Newey-West estimator.
56
CR IP T
ACCEPTED MANUSCRIPT
PT
ED
M
AN US
Figure 8: Cross-Correlation with GDP, High-Frequency Component
AC
CE
Note: Cross-correlations between the high-frequency component of the credit spread (left panel) and of the country interest rate (right panel) at quarter t − k with the high-frequency component of GDP at quarter k, in the data (black dotted line, with 95-percent confidence bands indicated by the shaded area) and in the model (blue circled line). Data confidence intervals obtained using the Newey-West estimator.
57
CR IP T
ACCEPTED MANUSCRIPT
Table 1: Standard Deviations, Annual Frequencies
GDP Consumption Investment NX / Y
Model
6.45 (5.08–7.81) 7.63 (5.76–9.51) 15.09 (11.51–18.67) 3.19 (2.28–4.10) 2.64 (1.88–3.41) 22.94 (18.56–30.39) 4.83 ( 3.11–6.55)
6.22 6.12 17.50 4.32 4.64
ED
Employment
Data
R&D
PT
TFP
9.60
2.75
Medium-frequency component 8-50
Data
Model
Data
Model
2.06 (1.40–2.72) 2.58 (1.35–3.81) 6.87 (4.49–9.25) 2.00 (1.23–2.77) 1.11 (0.64–1.58) 8.53 (4.62–14.73) 1.42 (0.95–1.89)
2.06
6.01 (4.70–7.33) 7.15 (5.54–8.76) 12.89 (9.22–16.55) 2.43 (1.46–3.41) 2.37 (1.64–3.10) 20.68 (15.43–28.22) 4.60 (3.11–6.09)
5.77
AN US
Variable
High-frequency component 0-8
Medium-term cycle 0-50
M
Frequency
2.50
7.96
2.43 1.84 5.27 0.84
5.46 15.16 3.44 4.16 7.78 2.57
AC
CE
Note: Standard deviations in the model and in the data of the medium-term cycle (frequencies between 0 and 50 years), the high-frequency component (0 to 8 years) and the medium-frequency component (8 to 50 years). Data 95-percent confidence intervals (computed using the Newey-West estimator) shown in parenthesis. Model standard deviations computed by averaging 10,000 simulations of a sample size corresponding to the data.
58
CR IP T
ACCEPTED MANUSCRIPT
Table 2: Financial Variables, Quarterly Frequencies (High-Frequency Component 2-32)
Credit Spread
Data
0.93 (0.47 – 1.40) 0.89 (0.64 – 1.14)
1.46
Data
Model
-0.28 (-0.45 – -0.10) -0.52 (-0.85 – -0.19)
-0.30
Country Interest Rate
M
Contemporaneous Correlation with GDP Credit Spread
ED
Model
AN US
Standard Deviation
PT
Country Interest Rate
0.89
-0.27
AC
CE
Note: Standard deviations and contemporaneous correlation with GDP of financial variables, in the model and in the data. Data 95-percent confidence intervals (computed using the Newey-West estimator) shown in parenthesis. Model moments computed by averaging 10,000 simulations of a sample size corresponding to the data.
59
A Model of Slow Recoveries from Financial Crises Albert Queralto PII: DOI: Reference:
S0304-3932(19)30054-6 https://doi.org/10.1016/j.jmoneco.2019.03.008 MONEC 3098
To appear in:
Journal of Monetary Economics
Received date: Revised date: Accepted date:
30 September 2016 15 March 2019 19 March 2019
Please cite this article as: Albert Queralto, A Model of Slow Recoveries from Financial Crises, Journal of Monetary Economics (2019), doi: https://doi.org/10.1016/j.jmoneco.2019.03.008
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Highlights • Output, productivity and innovation drop persistently following financial crises. • The paper develops model with endogenous TFP growth via innovation subject to financial frictions.
CR IP T
• The model accounts well for the South Korean evidence following the 1997 crisis.
AC
CE
PT
ED
M
AN US
• Financial frictions amplify significantly the TFP and output losses following the crisis.
1
ACCEPTED MANUSCRIPT
A Model of Slow Recoveries from Financial Crises∗ Albert Queralto† Federal Reserve Board
CR IP T
Abstract
Output and productivity fall persistently following financial crises, and innovation efforts also decline in these episodes. After reviewing the evidence, this paper introduces a quantitative macroeconomic model featuring endogenous growth in total factor productivity (TFP)
AN US
through innovation, in which innovators’ financing is subject to frictions. These frictions become more severe as financial sector balance sheets deteriorate, increasing the cost of credit for innovators and thereby lowering the growth rate of TFP. Key parameters are estimated using data from the South Korean 1997 financial crisis. Financial frictions are found to amplify significantly the medium-run TFP and output losses following the crisis.
1. Introduction
ED
JEL classification: E32; E44; F41.
M
Keywords: Business Cycles, Financial Crises, Total Factor Productivity
PT
A stylized fact of financial crises—well-exemplified by developments following the global crisis of 2008—is that their aftermath tends to be characterized by a slow recovery, resulting
∗
CE
in long-lasting damage to economic activity. Reinhart and Reinhart (2010) and Cerra and
AC
This paper is based on my dissertation at New York University. I am grateful to the co-editor Urban Jermann and an anonymous referee whose suggestions greatly improved the paper. I am especially indebted to Mark Gertler for his advice and guidance on this project. I also thank Ozge Akinci, Dario Caldara, Diego Comin, Martin Eichenbaum, Chris Erceg, Jordi Gal´ı, Matteo Iacoviello, Leyla Karakas, Robert Kollmann, John Leahy, Virgiliu Midrigan, Andrea Raffo, Mathias Trabandt, Vivian Yue and seminar participants at various venues for very useful comments and discussions. Alexander Mechanik, Patrick Moran and Dawson Miller provided outstanding research assistance. Financial support from Fundaci´ on Rafael del Pino is gratefully acknowledged. The views expressed in this paper are those of the author, and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of any other person associated with the Federal Reserve System. Earlier versions of this paper have circulated under the titles “A Quantitative Model of Slow Recoveries from Financial Crises” and “Financial Market Frictions, Productivity Growth and Crises in Emerging Economies.” † Division of International Finance, Federal Reserve Board. E-mail: [email protected]
Preprint submitted to Elsevier
March 22, 2019
ACCEPTED MANUSCRIPT
Saxena (2008), among others, have documented highly persistent drops in activity following severe financial crises, with little evidence of an eventual rebound of output back to trend.1 The first goal of this paper is to provide new evidence that may shed light on the behavior of output following financial crises. The paper documents that a large fraction of the highly persistent output declines is accounted for by persistent drops in productivity, and shows that
CR IP T
crises also tend to be associated with sharp slowdowns in businesses’ innovation expenditures. The declines in innovation efforts during banking crises are much sharper than in typical recession episodes, suggesting that the financial tightening that occurs during these crises may act as a drag on business innovation. In turn, the slower pace of business innovation may help account for the sustained productivity losses.
AN US
The second goal of the paper is to develop a quantitative model to address the evidence. The model introduces two modifications to a neoclassical economy. First, it explicitly accounts for the process of innovation as a source of medium-run productivity growth. Second, it includes an agency friction in financial markets that may disrupt the financing of investments in innovation. Thanks to these features, the model can capture a situation in which
M
a worsening of agency frictions—and the consequent tightening of credit—lowers the aggre-
persistent output losses.
ED
gate pace of innovation, thereby reducing trend productivity growth and leading to highly
The model features sustained growth in TFP arising due to an endogenously expand-
PT
ing variety of intermediates, as in Romer (1990). A competitive entrepreneurial sector has access to a technology that allows it to innovate, but lacks the funds to finance the neces-
CE
sary expenditures. To obtain funds, entrepreneurs borrow from banks. The outcome from entrepreneurs’ innovation efforts consists of novel varieties of intermediate goods, which are
AC
then used in final output production. To provide funding to entrepreneurs, banks borrow from both domestic households and
from international creditors. Banks are assumed to be efficient at monitoring entrepreneurial projects, so the relationship between banks and entrepreneurs is frictionless: entrepreneurs can offer banks perfectly state-contingent securities in exchange for funds. However, banks 1
See also Reinhart and Rogoff (2009); Ball (2014); or Chapter 4 in International Monetary Fund (2009) and references therein.
3
ACCEPTED MANUSCRIPT
face an agency friction in the process of obtaining funds from creditors. As in Gertler and Karadi (2011) and others,2 this friction takes the form of a limited enforcement problem: after borrowing funds, the bank can renege on its debt and divert a certain fraction of resources for its own personal gain, at which point creditors can force it into bankruptcy. The enforcement friction effectively introduces an endogenous constraint on the bank’s lending
CR IP T
to entrepreneurs.
The key determinant of the severity of financial constraints is banks’ net worth. The primary source of fluctuations in aggregate bank net worth is movement in the prices of assets on their balance sheets, which consist of claims on entrepreneurs. Similar to the financial accelerator model by Bernanke, Gertler, and Gilchrist (1999), in general equilibrium there is
AN US
feedback between the price of the equity issued by entrepreneurs and aggregate net worth, which acts as a powerful amplification mechanism: a decline in net worth first forces banks to cut back on project funding. This lowers the price at which new entrepreneurs can sell equity, and at the same time lowers the value of the currently outstanding entrepreneurial securities. In turn, this leads to additional declines in banks’ net worth, further tightening
M
credit to entrepreneurs.
The two features just described—endogenous growth through firm creation and frictions
ED
in financial intermediation—are embedded into a conventional small open economy model allowing for variable capital utilization and for a working capital requirement. These mod-
PT
ifications are standard in the small open economy business cycles literature. Although not critical for the main results on the persistent effects of financial crises, they help enhance
CE
the quantitative properties of the model at little cost of added complexity. The model’s main quantitative application consists in exploring its ability to account
AC
for the South Korean experience during the East Asian financial crisis. The motivation for focusing on Korea is threefold. First, Korea in the 1990s was a highly innovative economy— its ratio of R&D to GDP was among the highest in the world—and so the mechanism linking innovation to TFP growth was likely relevant.3 Second, there is broad agreement in the 2
See also Gertler and Kiyotaki (2010) or Gertler, Kiyotaki, and Queralto (2012). The ratio of R&D expenditures to GDP in Korea was 2.25% in 1996, close to that in the U.S. (2.44%) or Switzerland (2.45%) and above that of France, Germany and other European economies. Source: World Bank WDI. 3
4
ACCEPTED MANUSCRIPT
literature on the crisis trigger (namely, a sudden outflow of capital), facilitating the modeling of a crisis experiment, and also on the importance of financial stresses in exacerbating the crisis, consistent with the model. Third, the length of time elapsed since the crisis permits a clear assessment of the extent to which the crisis led to permanent losses in TFP and GDP, which the data suggest is large.
CR IP T
In the model, the crisis is triggered by an increase in the country risk premium, capturing the sudden stop that occurred in Korea in late 1997. The Korean evidence is used to estimate the model’s key parameters. In addition to a standard set of macroeconomic variables, I use two additional series to discipline the model’s key channels: business-sector expenditures on R&D, providing evidence on innovation, and a measure of private-sector credit spreads, a
AN US
proxy for the severity of financial disruption. The model can account well for the Korean data, particularly regarding the medium-run behavior following the crisis. Endogenous TFP growth via innovation is key for the model to generate the observed high persistence: it allows a crisis shock to induce a slowdown in the growth rate of TFP, leading to lasting effects on the real economy. Financial frictions play a quantitatively large role in generating
M
this persistence—with output, TFP, and other variables falling by much less when frictionless financial markets are assumed instead.
ED
In addition to the crisis episode, I also examine the model’s performance over the mediumterm cycle more generally, following the approach developed by Comin and Gertler (2006).
PT
I find that the model does a good job of reproducing the features of the Korean data over this dimension as well.
CE
The modeling approach in this paper builds on the literature on financial factors in macroeconomics, pioneered by Bernanke and Gertler (1989) and Kiyotaki and Moore (1997)
AC
and incorporated within a quantitative macroeconomic framework by Bernanke, Gertler, and Gilchrist (1999). The latest vintage of this class of models—which this paper follows most closely—focuses on financial intermediaries, as in Gertler and Karadi (2011). The key difference with this literature is that here financing frictions affect the introduction of innovations, which drives medium-run productivity developments—following the literature on endogenous growth due to Romer (1990). Support for the hypothesis that financing frictions affect innovation is provided in Section 2, which shows that private-sector R&D expenditure 5
ACCEPTED MANUSCRIPT
drops dramatically in recessions associated with financial crises—alongside highly persistent TFP declines—while it falls by much less in recessions that are not associated with financial crises. This paper is also related to a growing literature incorporating endogenous growth mechanisms following Romer (1990) within quantitative macroeconomic frameworks—notably
CR IP T
Comin and Gertler (2006), who propose a model to study medium-term cycles in the United States, and Bilbiie, Ghironi, and Melitz (2012), who analyze producer entry over the business cycle. Comin, Gertler, and Santacreu (2009) estimate a version of the model in Comin and Gertler (2006), and Comin, Loayza, Pasha, and Serven (2014) propose a related framework for developing countries. A very recent set of papers uses frameworks in this spirit to
AN US
analyze the U.S. Great Recession, and U.S. fluctuations more generally. Examples include Anzoategui, Comin, Gertler, and Martinez (2016), Guerron-Quintana and Jinnai (2014), Bianchi and Kung (2014), and Moran and Queralto (2018). This paper differs partly by explicitly modeling financial frictions affecting innovators, and partly by using measures of private-sector R&D expenditures and credit spreads in the quantitative analysis.4
M
The evidence and model presented here are related to Aguiar and Gopinath (2007), who argue that a distinctive feature of emerging market business cycles is large movements
ED
in trend TFP growth. The evidence in this paper suggests that financial crises are one instance in which such non-stationary behavior occurs. In addition, the model introduced
PT
below provides a foundation for the type of TFP process taken as exogenous by Aguiar and Gopinath (2007) and connects it explicitly with financial market imperfections—thereby
CE
showing how the latter amplify the volatility of the nonstationary component of TFP. This paper also relates to a large literature that proposes quantitative macroeconomic
AC
models for emerging market economies, such as Uribe and Yue (2006) and Neumeyer and Perri (2005). A related branch of this literature has proposed quantitative frameworks to account for financial crises and sudden stops in emerging markets, like Gertler, Gilchrist, and Natalucci (2007), Mendoza (2010) and Mendoza and Yue (2012). Some of these frameworks, as well as several others, offer explanations for the observed decline in TFP during financial 4
Anzoategui, Comin, Gertler, and Martinez (2016) also use private-sector R&D, but do not model financial frictions.
6
ACCEPTED MANUSCRIPT
crises, including declines in capacity utilization, sectoral reallocation, and disruption in trade of imported inputs.5 While these mechanisms and the one introduced in this paper can be seen as complementary, the key difference is that the focus here is on accounting for mediumrun TFP declines, and their role in generating the persistent output effects identified in the data. This focus also differentiates the present paper from other quantitative analyses of the
CR IP T
Korean 1997 crisis, like Gertler, Gilchrist, and Natalucci (2007), Benjamin and Meza (2009), and Otsu (2008).
The model developed in this paper connects with a recent literature linking financial frictions with producer heterogeneity to account for fluctuations in TFP. For example, Sandleris and Wright (2014) and Oberfield (2013) explore how resource misallocation across
AN US
producers can account for the observed movements in TFP following the financial crises in Argentina in 2001 and Chile in 1982, respectively. Motivated by these studies, Buera and Moll (forthcoming) use a canonical model with firm heterogeneity and collateral constraints to explore how a credit crunch may translate into a drop in measured aggregate TFP (and other aggregate “wedges”). Gopinath, Kalemli-Ozcan, Karabarbounis, and Villegas-Sanchez
times in the euro area periphery.
M
(2015) use a model in this class to explain the slow pace of productivity growth in recent
ED
The present paper shares the goal with the literature above of connecting TFP with financial frictions, but focuses on how such frictions affect the extensive margin of firm cre-
PT
ation while abstracting from the intensive margin of reallocation among existing producers. This is done partly for tractability: the resulting model delivers simple aggregation, and can
CE
be integrated within a standard macroeconomic framework in a straightforward way. At the same time, the focus on the extensive margin is consistent with the empirical findings
AC
in Midrigan and Xu (2014), who use firm-level data from emerging economies to document that financial frictions cause much greater losses by distorting firm entry and technology adoption than by inducing misallocation across producers. While Midrigan and Xu (2014) largely focus on how financial frictions may lead to aggregate TFP losses over the long run, the present paper focuses on capturing medium-run dynamics following a financial crisis, and 5
For instance, Gopinath and Neiman (2014), Benjamin and Meza (2009), Meza and Quintin (2007), Kehoe and Ruhl (2009), Pratap and Urrutia (2012), and Aoki, Benigno, and Kiyotaki (2009).
7
ACCEPTED MANUSCRIPT
on developing a tractable model that can capture a broad set of macroeconomic variables. In recent, independent work, Ates and Saffie (2016) build an endogenous growth model with financial frictions to study dynamics following sudden stops. In addition to important differences in the modeling approach (for example, the present framework endogenizes financial constraints as arising from an explicit agency friction) there is a significant difference
CR IP T
in focus between the two papers—Ates and Saffie (2016) emphasize selection effects among entrants, while the present paper’s main focus is on the link between elevated financial stress and persistent TFP losses.
The rest of the paper is organized as follows. Section 2 presents evidence on the behavior of output, productivity, and innovation in financial crises, and briefly describes the Korean
AN US
experience. Section 3 describes the model. Section 4 presents a quantitative analysis of the model, which consists of simple experiments designed to illustrate its workings as well as an application to the Korean data. Section 5 provides concluding remarks.
M
2. Evidence
This section documents highly persistent productivity and TFP losses associated with
ED
financial crises—using a panel of both advanced economies (AEs) and emerging market economies (EMEs)—and it investigates the behavior of business innovation during these events. This is done in subsection 2.1. I review the evidence on the 1997 Korean financial
PT
crisis in subsection 2.2.
CE
2.1. GDP, Productivity and Innovation in Financial Crises The empirical approach follows Cerra and Saxena (2008), who estimate a univariate
AC
autoregressive model for output growth—augmented to include current and lagged dummies indicating financial crises and other shocks—on a panel of countries. I estimate the following model:
xi,t = αi +
J X
β j xi,t−j +
j=1
L X
δ l Di,t−l + εi,t
(1)
l=0
Above, xi,t is the value of the variable of interest in country i and year t, and Di,t is a dummy variable indicating a financial crisis or a recession. As in Cerra and Saxena (2008), 8
ACCEPTED MANUSCRIPT
the specification includes a country fixed effect, αi . The first set of results involves a decomposition of (log) output as the sum of productivity and hours: Yi,t Ni,t
+ log (Ni,t )
Here Yi,t is real GDP, Ni,t is hours worked, and
Yi,t Ni,t
(2)
CR IP T
log (Yi,t ) = log
is productivity. I then estimate
(1) for (first-differenced) log output, productivity and hours separately. Based on the recommendations of AIC and BIC tests as well as the “general-to-specific sequential t rule” (see Chapter 6 in Hayashi (2000)), I set J = 1 and L = 5, although results are robust to
AN US
reasonable variations of the lag structure. The dataset consists of a panel of 36 countries starting in 1950, and includes 50 banking crisis episodes as dated by Laeven and Valencia (2013). All crisis episodes are in the 1970s and later, and they are evenly split between AEs and EMEs.6
The three panels in Figure 1 show the estimated effect of banking crises on the three
M
variables—GDP, hours, and productivity. The decline of output in a banking crisis is very large and persistent, with GDP falling almost 15 percent after five years. Consistent with
ED
the findings in Cerra and Saxena (2008), the GDP losses are never reversed. Turning to the decomposition of GDP, there is highly persistent damage on both productivity and hours.
PT
The key point is that the damage on productivity is larger than on hours: the latter decline by slightly less than 7 percent by the tenth year, while productivity falls 9 percent.
CE
Figure 1 also provides the impulse responses for the AE and EME subsamples separately (the sample contains 25 crisis episodes in AEs and 25 in EMEs). The finding that the output damage is long-lasting holds for both subsamples. Interestingly, the adverse effects for all
AC
three variables are more severe in the AEs—a result that strongly reflects the severity of the 2008 global financial crisis affecting many AEs. In addition, the finding that productivity is at least as important as hours in accounting for the GDP decline also holds for each of the two separate subsamples. Why do productivity and GDP remain persistently depressed following financial crises? 6
Online Appendix E contains the full list of crisis episodes.
9
ACCEPTED MANUSCRIPT
The key channel in the model presented below posits that the tightening of financial constraints that occurs in banking crises leads to a reduction in business innovation, which then causes the persistent slowdown in productivity. To explore this channel empirically, in the next specification I use data on business-sector R&D expenditures as a proxy for innovation.7 I then compare the behavior of R&D, TFP and GDP during recessions associated
CR IP T
with banking crises, with their behavior during other types of recessions. The idea is that banking crises likely feature especially severe financial tightening, compared with recessions not associated with banking crises. In this specification, the sample consists of 34 banking crisis episodes among both AEs and EMEs, and 22 episodes of recessions not associated with banking crises.8
AN US
The first panel in Figure 2 shows that while R&D only falls slightly during a normal recession, banking crises precipitate an enormous drop in R&D, with no evidence of a subsequent turnaround. At the same time, the middle panel shows that TFP follows a pattern very similar to R&D: normal recessions are associated with only a small and transient decline while banking crises feature a large and persistent fall. Similarly, the decline of output in a
M
banking crisis (shown in the last panel) is very large and persistent, with GDP falling more than 10 percent below trend after 5 years; by contrast, output falls only about one third as
ED
much during other recessions.9
Robustness.— The empirical specification (1) assumes that output growth (and the other
PT
response variables considered) can react contemporaneously to a banking crisis, while the occurrence of the crisis is exogenous to current-year output growth. An alternative possi-
CE
bility is that growth is contemporaneously exogenous to the crisis, and the crisis affects it only with a lag. To test for this possibility, I repeat the analysis above setting δ 0 = 0 in
AC
equation (1). The specification is left otherwise unchanged (i.e. same number of lags for 7
I use R&D data because it has the largest coverage in terms of countries and years. Like R&D, the number of new business startups—another proxy for innovation—also shows sharp declines in financial crises. Empirical results are available upon request. 8 I obtain international recession dates from the Economic Cycle Research Institute (ECRI), and then eliminate those ECRI dates that are associated with a banking crisis to create an index of recessions not associated with banking crises (labeled “other recessions”). Appendix E contains the full set of episodes. 9 The behavior of output in Figures 1 and 2 is not exactly the same due to differences in the sample. The sample used to obtain the results in Figure 2 is shorter, a result of the more limited availability of data on business-sector R&D.
10
ACCEPTED MANUSCRIPT
the endogenous variable and for the crisis dummy). As expected, I find that when growth is contemporaneously exogenous to the crisis, the overall responses become somewhat smaller in magnitude. For example, productivity falls about 6 percent after six years, compared to almost 9 percent in Figure 1. The key results, however, remain robust to the alternative specification: all three variables in Figure 1 continue to fall persistently (and remain signif-
CR IP T
icantly below zero with 95 percent confidence), and productivity falls more than hours. As in Figure 2, R&D and TFP continue to fall significantly following banking crises.10
A potential concern with fixed-effects estimation of (1) is inconsistency of the parameter estimates due to the combination of fixed effects and lagged dependent variables (Nickell (1981)). However, because the time series dimension of the data is large, the inconsistency
AN US
problem is likely not a major concern. Appendix A confirms that this is the case by repeating the analysis using the bias correction method by Bruno (2005). The bias-corrected impulse responses are very close to the baseline results, and lie well within the confidence intervals in Figures 1 and 2.11
M
2.2. The 1997 Korean Financial Crisis
This section provides a brief description of the Korean financial crisis of 1997, which is
ED
later used as a quantitative application of the model. The Korean crisis started in the fourth quarter of 1997, following crises in Thailand and other countries in the region. Until then,
PT
the 1990s had been a time of robust growth for the Korean economy. The onset of the crisis was marked by a sudden and massive outflow of capital, mirrored by a sharp rise in the
CE
country risk premium. The capital flight and the ensuing crisis were largely unanticipated.12 The crisis then spilled over to the real economy, which started contracting in the fourth
AC
quarter of 1997 and accelerated its decline in the first quarter of 1998. The blue solid line in Figure 3 shows the behavior of the Korean economy over this period.
In addition to a standard set of macroeconomic variables, the Figure includes business10
These results can be found in the Online Appendix A, Figures (A.1)-(A.2). See Figures (A.3)-(A.4) in Appendix A. 12 According to OECD projections, the Korean economy was projected to continue on its path of robust growth as late as June and even December of 1997 (see Appendix B, Figure B.1). Radelet and Sachs (1998), Krueger and Yoo (2002), and Furman and Stiglitz (1998) also highlight the unexpected nature of the East Asian financial crisis. 11
11
ACCEPTED MANUSCRIPT
sector R&D and TFP (both available only at the annual frequency), as well as a credit spread to proxy for the extent of financial tightness.13 The credit spread is constructed as a weighted average (with equal weights) of two spreads: the spread between Korean 3-year AA- corporate bonds and 3-year Treasury bonds, and the spread between Korean (marginal) bank lending rates and Treasury bonds. The resulting weighted average is likely a better
CR IP T
measure of financial tightness affecting innovators than the simple corporate credit spread, since many firms (particularly startups and small firms) do not have access to the corporate bond market. The equal weights on each spread are chosen to reflect the fact that the value of listed corporate bonds and of aggregate bank loans to the (non-household) non-financial sector around the crisis were roughly equal in magnitude (at a little over 100 trillion won
AN US
each).14
Figure 3 also includes a range of projections based on pre-crisis trends, shown by the blue shaded areas, calculated following the approach in Christiano, Eichenbaum, and Trabandt (2015).15 These projections are computed as follows: for each variable I fit a linear trend from date x to 1997:III, where x ∈ {1985:I, 1995:IV}. For the variables available annually,
M
I use 1997 as the final date and x ∈ {1985, 1995} as the start date. For each x, the linear trend is then extrapolated from 1997:IV onward. The blue shaded areas in Figure 3 represent
ED
the min-max range for the trend projections. The range of projections is a transparent and plausible way to estimate how the economy would have evolved in the years following 1997,
PT
absent the large shocks associated with the financial crisis. The deviations of the actual
13
CE
values with respect to these trends are later used to characterize the impact of the crisis,
AC
Output, consumption, investment, employment and R&D are in real per capita terms. All variables are shown as logs except net exports-to-GDP (NX/Y) and the credit spread, which are shown in levels. 14 Source: Haver Analytics for listed corporate bonds and Bank of Korea for bank loans. 15 Hall (2015) and Blanchard et al. (2015) have recently used similar approaches.
12
ACCEPTED MANUSCRIPT
and to compare the effects of a model-simulated crisis to their empirical counterparts.16,17 As Figure 3 makes clear, the years prior to the crisis were marked by fast GDP and TFP growth (GDP grew at about 6 percent annually on average between 1990 and 1997, and TFP at 4 percent), and remarkably mild fluctuations. The 1997 crisis then led to sharp contractions in the real economy, including TFP, as well as a massive and sudden reversal in
CR IP T
the net exports to GDP ratio. As shown in the bottom left panel, firms’ innovation collapsed, at the same time as credit spreads spiked to unprecedented levels (bottom right panel). The crisis represented a clear “trend break” for the real economy, including (crucially) for TFP: after the crisis all macroeconomic variables remained persistently depressed relative to the range of pre-crisis projections.
AN US
The black line in Figure 5 shows the average deviations in the Korean data after 1997:III, and the gray shaded areas indicate the min-max ranges of these deviations. The gaps between actual and projected values were largely not reversed. Output contracted persistently by almost 15 percent relative to the projection, close to the average permanent output loss in the sample of banking crisis episodes discussed in section 2.1.18 The gap for employment
M
was around 8 percent in the medium run, and TFP fell by about 7 percent relative to the projection. Consumption, and especially investment, declined by much more. Note also
ED
that the drop in consumption was substantially larger than that of output, consistent with the stylized fact that consumption in emerging economies is more volatile than output.19
PT
Mirroring the capital outflow, there was a dramatic reversal in the current account balance, with the ratio of net exports to GDP increasing by more than 15 percent (relative to the
16
CE
projection) in just two quarters. Business-sector R&D fell persistently by more than 30
AC
One possible caveat to the use of this approach is that it may not be plausible to project that the Korean economy would grow indefinitely at an average rate of over 6% (as it did in the 1990s and earlier), even in the absence of the crisis. While it is true that Korean growth eventually converged to levels closer to advanced-economy averages, Figure B.2 in Appendix B shows that in the 5-year period following the crisis trough, average growth was similar to the levels observed prior to the crisis. A second possible objection is that pre-crisis growth in 1990-97 might have been abnormally fast, due to an unsustainable boom fueled by capital inflows. As Table B.1 in Appendix B shows, however, the years before the crisis in which the current account deficit widened did not coincide with an acceleration in growth. 17 A previous version of the paper used ARIMA methods to form pre-crisis projections. Appendix B compares that method to the present one. 18 Cerra and Saxena (2005) find a similar magnitude for the permanent output loss following the Korean crisis, using a different methodology (see their Table 4). 19 See Aguiar and Gopinath (2007), for example.
13
ACCEPTED MANUSCRIPT
percent, and the credit spread rose by 700 basis points. Appendix B includes the behavior of additional proxy measures of innovation and firm creation: the number of business owners per capita (a measure of entrepreneurship, as argued in Stel (2008)), and patent and trademark applications by Korean residents. Like R&D, all three indicators decline sharply in 1998, after rising for almost two decades practically
CR IP T
without interruption. These declines are consistent with the findings in Midrigan and Xu (2014), who find a sharp drop in the number of producers operating during the crisis in the Korean manufacturing sector using an establishment-level dataset covering all manufacturing plants (with more than five workers).20
The evidence thus supports the key mechanism introduced in this paper, namely that a
AN US
financial crisis may lead to a reduction in the pace of innovation, resulting in highly persistent effects of the crisis. Several other authors have documented empirically an adverse effect of financing frictions on firm creation and technology innovation and adoption, both in the U.S. and other OECD countries21 as well as in developing economies.22
In sum, the Korean financial crisis appears to have been triggered by a sudden and
M
unanticipated withdrawal of foreign inflows. This led to a severe tightening of domestic financial conditions, and was associated with sharp losses in the real economy that were
ED
largely not reversed. TFP fell substantially and persistently relative to trend, with evidence suggesting a role for business innovation in accounting for the persistent TFP loss. The
PT
following section introduces a model featuring this mechanism and explore its ability to account for the salient facts of the Korean crisis, with an emphasis on capturing the medium-
20
CE
run declines.
See panel B in Figure 4 of Midrigan and Xu (2014). Kortum and Lerner (2000) establish a positive effect in the U.S. on innovation of the availability of venture capital funding, and Kerr and Nanda (2009) show that U.S. financial reforms enhanced the process of small firm entry. See also Hall (2002) and Hall and Lerner (2009) for surveys of work analyzing the impact of limited access to external finance on innovation and R&D using firm-level data from OECD countries. Ridder (2017) provides evidence that tight financial conditions reduced productivity-enhancing investments in the US during the global financial crisis. 22 For example, Ayyagari et al. (2012) and Gorodnichenko and Schnitzer (2013).
AC
21
14
ACCEPTED MANUSCRIPT
3. Model The core framework is a small open economy with endogenous TFP growth through an expanding variety of intermediates, as in Romer (1990) and Comin and Gertler (2006). The key difference with respect to these frameworks is that there is an imperfection in financial
CR IP T
markets that impedes the smooth flow of resources from savers to banks, who finance the development of innovations. Two additional features are introduced to enhance the model’s quantitative performance: variable capital utilization and a working capital requirement for intermediate goods producers. To avoid additional complexity, the model abstracts from nominal wage and price rigidities and monetary factors—though as in Comin and Gertler
AN US
(2006), it allows for exogenous markup variation as a stand-in for the effects of those frictions. There are four types of agents in the model: households, entrepreneurs, banks, and goods producers. Homogeneous final output is produced using an expanding variety of intermediates. Entrepreneurs use funds borrowed from banks to finance the creation of new varieties. In turn, banks obtain financing from domestic households and from international
M
capital markets. The following sections discuss the behavior of each of these agents.
ED
3.1. Households
The economy is populated by a continuum of measure one of families, and each family h has a unit measure of members. Households make decisions on consumption, labor supply,
PT
investment in physical capital and saving through a risk-free one-period international bond. There are two types of members within each household: workers and bankers, with measures
CE
1 − f and f respectively.23 A fraction of the workers are specialized—or “skilled”—workers who supply labor inelastically to entrepreneurs. Regular workers in family h are monopolistic
AC
suppliers of a differentiated h-specific labor type, used to produce intermediate goods.24 Both types of labor return wages to the family. Bankers manage a financial intermediary that uses borrowed funds to make loans to entrepreneurs. There is perfect consumption insurance among family members. 23
As in Gertler and Karadi (2011), this formulation is a simple way of introducing heterogeneity in terms of borrowers and lenders while maintaining the tractability of a representative agent model. 24 Differentiated labor is introduced to allow for (exogenously) time-varying market power in labor supply, which will induce movement in wage markups.
15
ACCEPTED MANUSCRIPT
There is random turnover between bankers and workers: a banker becomes a worker with probability 1 − σ, at which time he or she transfers accumulated earnings to the family.
f Workers become bankers with probability (1 − σ) 1−f , so there is a measure (1 − σ)f of
new bankers each period—exactly offsetting the number that exit. The family transfers a small amount of resources to new bankers so they are able to start operations. Banker
CR IP T
exit is introduced as a device to ensure that the financial imperfection will remain relevant— otherwise banks might reach a point where internal resources are enough to finance all desired lending.25
Letting Ct denote consumption and Lht hours of work in the intermediates sector, household h’s utility function is 1−ρ
AN US
1 Ct − Γt 1+ L1+ ht U (Ct , Lht ) = 1−ρ
−1
(3)
The preference structure above is common in the emerging market business cycles literature.26 It abstracts from wealth effects on labor supply, as in Greenwood et al. (1988) (GHH
M
henceforth): by assuming that utility is a function of the excess of consumption relative to the disutility of labor, the marginal rate of substitution between consumption and labor
ED
becomes independent of consumption. The specification ensures that increases in country interest rates do not lead to counterfactual booms in employment and output via positive
PT
movements in labor supply.27
The term multiplying the disutility of work, Γt , is assumed to depend on the aggregate
CE
technological level, At , as follows: Γt = Aγt Γ1−γ t−1
(4)
AC
where γ ∈ (0, 1). The emerging market business cycle literature typically features preferences with Γt = 1 for all t. In these frameworks, however, there is usually no long-run growth. In the present framework with endogenous productivity growth, constant Γt would imply trend growth in hours worked. The presence of Γt ensures a balanced growth path with 25
Exogenous exit can also be interpreted as a way of motivating dividend payouts by banks. For example, Mendoza and Yue (2012), Mendoza (2010), or Uribe and Yue (2006). 27 See, for example, the discussion in Neumeyer and Perri (2005). 26
16
ACCEPTED MANUSCRIPT
constant hours. At the same time, so long as γ is small, fluctuations in At will have a small impact on labor supply at medium frequencies, consistent with the usual formulation of GHH preferences.28,29 A large number of competitive “employment agencies” combine specialized labor into a
Lt =
Z
1
0
1/µ Lht wt dh
µwt
CR IP T
homogeneous labor input used by intermediate goods producers, according to (5)
The variable governing the elasticity of substitution across labor types, µwt , follows a firstorder autoregressive process: µ ˆ wt = ρµ µ ˆ wt−1 + µt , where hats denote log-deviations from mization, demand for labor variety h is
Lht = R
1 0
1/(1−µwt )
Wht
1−µwt dh .
Wht Wt
µwt − 1−µ
wt
Lt
(6)
M
where Wt =
AN US
steady state, and with µt an iid random variable. From employment agencies’ cost mini-
Household h’s problem is to choose stochastic sequences for consumption Ct , labor supply
ED
to the intermediates sector Lht , debt Dt , following-period capital Kt+1 , net investment Itn ,
28
CE
PT
and capital utilization ut to solve the following problem:30 max
{Ct+i ,Lht+i ,Dt+i , ∞ n ,u Kt+i+1 ,It+i t+i }
i=0
Et
∞ X
β i U (Ct+i , Lht+i )
(7)
i=0
Jaimovich and Rebelo (2009) propose a specification with similar properties. Under the interpretation of GHH preferences as a reduced form for an economy with home or nonmarket production, as in Benhabib et al. (1991), a small value of γ might be viewed as capturing a process of “slow diffusion” of the varieties used in the business sector into the home sector. As long as γ is positive, home productivity would still grow at the same rate as At at low frequencies, precluding households from perpetually supplying more labor to the business sector. 30 Because all households h will end up making the same decisions on consumption, investment, utilization, and capital and bond holdings, the subindex h from these variables is omitted at the onset.
AC
29
17
ACCEPTED MANUSCRIPT
subject to Ct + Itn + δ(ut )Kt + Rt−1 Dt−1 ≤ rtk ut Kt + Wht Lht + WSt LS + Dt + τ t Kt+1 ≤ Kt + Υ(Itn /Kt )Kt
(8) (9)
CR IP T
and (6) for all t. Here, δ(ut ) denotes the capital depreciation rate, Rt−1 is the gross interest on the non-contingent international bond from t − 1 to t, rtk is the rental rate on capital services, Wht is the wage rate for labor type h, WSt is the skilled labor wage, LS is the supply of skilled labor, and τ t denotes net transfers from the financial intermediation sector.
In addition to accumulating physical capital, households set its utilization rate:31 they
AN US
convert their stock of capital Kt into capital services ut Kt , which are rented out to intermediates firms at rental rate rtk . The capital depreciation rate δ(ut ) is an increasing and convex function of the utilization rate:
a 1+δ ut 1+δ
(10)
M
δ(ut ) = ˜δ +
The household’s uses of funds (the left hand side of (8)) include consumption, net investment,
ED
repair of depreciated capital, and debt repayments. The household’s sources of funds (the right hand side of (8)) are earnings from renting capital services, labor income, new debt
PT
issues, and net transfers from bankers.
Households combine units of the final output Itn with existing capital Kt to produce new
CE
capital goods, using the constant returns to scale production function Υ(Itn /Kt )Kt . The function Υ(Itn /Kt ) is increasing, concave, and assumed to satisfy Υ (in ) = in and Υ0 (in ) = 1, where in is the net investment-capital ratio along the balanced growth path. The concavity
AC
assumption introduces costs of adjusting the level of the capital stock. As made clear by (8)(9), repair of depreciated capital does not entail adjustment costs, an assumption commonly made in the literature that ensures that the capital utilization decision does not depend on the (shadow) price of capital. 31
I assume households make capital utilization and accumulation decisions for convenience. At the cost of additional notation, it is possible to use a decentralization in which those decisions are made by firms that yields identical equilibrium conditions.
18
ACCEPTED MANUSCRIPT
The country interest rate Rt is the product of an exogenous world interest rate, R∗ , and a country borrowing premium Ψt : Rt = R∗ Ψt . The premium is assumed to depend on total domestic debt held by foreign investors—denoted Bt —relative to trend, and on a stochastic Bt S −d Ψt = ert + ψ e At − 1
(11)
CR IP T
disturbance rtS :
As usual in the small open economy literature, the reason for introducing a positive dependence of the cost of borrowing on foreign indebtedness is to ensure stationary dynamics around the trend. The parameter ψ is set to a very small value so that this feature does not affect the medium-run dynamics of the model. The random disturbance rtS is assumed to
AN US
S + rt , where rt is an iid innovation. follow a first-order autoregressive process: rtS = ρR rt−1
A positive innovation to rtS , interpretable as a rise in the country risk premium, is a simple way to model the sudden capital outflows that initiated the Korean crisis. Each household h sets the wage according to
M
µwt Γt Lht = Wht
ED
The variable µwt represents a markup of the wage over the household’s marginal rate of substitution between labor and consumption. Note that each household will charge the same wage and supply the same amount of labor, a condition that is imposed henceforth.
PT
Let UCt denote marginal utility of consumption and Λt,t+i the household’s stochastic
AC
CE
discount factor, given by the equations βUCt+i UCt −ρ 1 1+ = Ct − Γt L 1+ t
Λt,t+i =
(12)
UCt
(13)
and let PKt be the shadow price of capital, i.e. the Lagrange multiplier on constraint (9) divided by UCt . Then the household’s first-order conditions for Dt , Kt+1 , Itn , and ut are the
following: 1 = Et (Λt,t+1 ) Rt
(14) 19
ACCEPTED MANUSCRIPT
(
k ut+1 − δ(ut+1 ) + PKt+1 rt+1 1 = Et Λt,t+1 PKt " #−1 Itn PKt = Υ0 Kt
)
rtk = δ 0 (ut )
(15) (16)
CR IP T
(17)
where equation (15) omits terms that are of second order.32
Equations (14) and (15) are two conventional bond and capital Euler equations. Equation (16) implies that the shadow price of capital PKt is an increasing function of the ratio Itn /Kt . Equation (17) sets the marginal depreciation rate equal to the capital rental rate,
AN US
the marginal benefit of utilization. 3.2. Entrepreneurs
A representative competitive entrepreneur uses skilled labor and final output as inputs to produce new product varieties, or innovations. An innovation is interpreted as the full design
M
of a distinct product variety, including all the instructions needed to manufacture it. The model makes no distinction between entirely novel products or possible adaptations made
ED
by entrepreneurs in the small open economy of products already in use in more advanced countries. The assumption is that adapting to the local economy an innovation from a
PT
more developed country still requires costly development efforts.33 As described in section 3.4, final output is produced by combining the existing varieties of intermediates. The entrepreneurial sector can be interpreted as capturing innovation broadly defined, including
CE
business start-ups.34
Let At be the total number of firm varieties in operation in period t, and Zt the number of
AC
new varieties created in period t. Both existing and newly created varieties face the risk of an 32
n h n i o n I It+1 k The optimality condition is 1 = Et Λt,t+1 rt+1 ut+1 −δ(ut+1 )+PKt+1 +PKt+1 Υ Kt+1 − /P . Kt K t+1 t+1
Given the assumptions on Υ(·), which ensure there are no adjustment costs along the balanced growth path, In
In
the term PKt+1 Υ Kt+1 − Kt+1 is zero along the balance growth path and to a first order around it. t+1 t+1 33 Mansfield et al. (1981) provide evidence that imitation costs are substantial. 34 An alternative, equivalent formulation of the entrepreneurial sector would consist of an unbounded mass of potential entrants, each endowed with one idea for a potential new variety, which can be developed by paying a sunk entry cost given by the marginal cost associated with the innovation production function described below.
20
ACCEPTED MANUSCRIPT
exogenous exit (or obsolescence) shock: any variety becomes obsolete with probability 1 − φ. The exit shock occurs at the end of the period, after production and entry, so proportion 1 − φ of new innovations will never be active.35 Accordingly, the evolution of the aggregate stock of innovations, At , is given by: (18)
CR IP T
At+1 = φ (At + Zt )
The entrepreneur operates a Cobb-Douglas production function that transforms skilled labor, LSt , and final output, Nt , into innovations Zt :
(19)
AN US
Zt = Ntη (At LSt )1−η
As in Romer (1990), (19) incorporates a positive externality from the aggregate technological level, At , on the efficiency of skilled labor in developing innovations. This assumption is key to generate endogenous growth. The total input cost of the entrepreneur, interpreted as
M
R&D expenditure, is Rt ≡ Nt + WSt LSt , where WSt is the skilled labor wage. Once the entrepreneur has created an innovation, he or she is granted the sole ownership
ED
of the new product design. The entrepreneur then rents the design to monopolists who manufacture the new good, for a rental rate π t each period. In equilibrium, π t will equal the profits of monopolist producers, as described in section 3.4. Thus, an innovation developed
PT
in period t is an asset which provides the entrepreneur a payoff stream given by the random sequence {π t+i }∞ i=1 , so long as the innovation does not become obsolete.
CE
The entrepreneur has no internal funds at the beginning of period t. To cover R&D costs, he or she obtains financing from banks. There are no contracting frictions between
AC
entrepreneurs and banks. The representative entrepreneur obtains funds in period t by selling Zt securities—one for each innovation created—in a market in which banks are the buyers. These securities pay π t+i in each future period t + i > t if the underlying innovation is still in
operation (and zero if the variety has become obsolete). The price of one of these securities is denoted Jt . 35
The timing assumption follows Bilbiie et al. (2012).
21
ACCEPTED MANUSCRIPT
The entrepreneur thus makes zero profits, state-by-state, in all future periods. In period t, he or she obtains revenues Jt Zt from the sales of securities, and pays the input cost Nt + WSt LSt . The entrepreneur’s problem in period t is Jt Zt − Nt − WSt LSt
subject to (19). It follows that (see Appendix F for details) η 1−η 1 WS,t /At Jt = η 1−η
(20)
CR IP T
max
Zt ,Nt ,LSt
(21)
AN US
The right hand side of (21) is the marginal cost of innovation. Equation (21) states that this marginal cost equals the price of entrepreneurs’ equity, Jt . Equity sales exactly cover R&D expenditures, and the entrepreneur makes zero profits in period t: Jt Zt = Nt + WSt LSt . The amount of final output used in innovation is given by
(22)
M
Nt = ηJt Zt
ED
Skilled-labor market clearing implies LSt = LS . As shown in Appendix F, combining this condition with (21) and the first-order condition for LSt from (20) yields a simple and intuitive relation between the price of entrepreneur equity Jt and the number of new varieties
CE
PT
Zt :
1 Jt = η
1 Zt LS At
1−η η
(23)
AC
Equation (23) can be interpreted as an aggregate supply curve of innovations, with aggregate product creation relative to the stock of innovations
Zt At
an increasing function of the price
of equity Jt . 3.3. Banks
Financial intermediaries (“banks,” for short) lend funds obtained in capital markets to entrepreneurs. Banks are interpreted as specialists who assist in channeling funds from savers to entrepreneurs. They are meant to capture the entire financial intermediation sector, i.e. 22
ACCEPTED MANUSCRIPT
investment and savings institutions as well as traditional commercial banks.36 Banks’ assets consist of securities issued by entrepreneurs, and their liabilities include short-term debt and net worth. There is an agency friction between banks and their creditors which takes the form of a limited enforcement problem, modeled as in Gertler and Karadi (2011) and Gertler and Kiyotaki (2010): at the end of the period, after borrowing funds, a
CR IP T
bank can default on his or her debt and divert an exogenous fraction θ of resources. This imposes a limit on how much leverage banks are able to take on ex-ante. The optimization problem of a continuing banker j is the following: Et
σ
i−1
i=1
h i (1 − σ)Λt,t+i φ (π t+i + Jt+i ) Sjt+i−1 − Rt+i−1 Djt+i−1
AN US
max
{Sjt+i−1 ,Djt+i−1 }∞ i=1
(∞ X
subject to
Jt Sjt + Rt−1 Djt−1 ≤ φ (π t + Jt ) Sjt−1 + Djt
i=1
σ i−1 (1 − σ)Λt,t+i [φ (π t+i + Jt+i ) Sjt+i−1 − Rt+i−1 Djt+i−1 ] ≥ θJt Sjt
(24)
(25)
(26)
M
Et
∞ X
)
From the budget constraint (25), the bank’s use of funds (left hand side) includes the pur-
ED
chase of Sjt claims on entrepreneurs, which cost Jt Sjt , as well as debt repayments, Rt−1 Djt−1 . The sources of funds are the revenues obtained from securities purchased the previous period
PT
(the first term on the right hand side) as well as new debt issued, Djt . Of the securities purchased last period, Sjt−1 , fraction 1 − φ receive the obsolescence shock. The remainder
CE
generate payoff π t for the bank and can be sold on the market at price Jt . As indicated by (24), if a bank alive at t exits in subsequent period t + i (which occurs
AC
with probability σ i−1 (1 − σ)), it transfers the accumulated wealth back to the household. The net resources accumulated until period t + i are given by the term in square brackets in
(24). The bank values payoffs in the period and state at which it exits using the stochastic
36 Investment institutions and other “shadow” financial intermediaries played an increasingly important role in the Korean financial sector in general, and in lending to small firms in particular, in the years prior to the 1997 crisis. For example, securities dealers—a subset of these investment institutions—promoted the establishment of the KOSDAQ equity market in 1996, as a means of financing venture businesses and small firms. See Bali˜ no and Ubide (1999) for a description of the Korean financial sector before the crisis.
23
ACCEPTED MANUSCRIPT
discount factor Λt,t+i . Equation (26) is the bank’s incentive constraint. At the end of period t, after having borrowed in the debt market, the bank may choose to default on its creditors and divert fraction θ of available funds, which it transfers back to the household. Creditors can then force the bank into bankruptcy and recover the remaining fraction 1 − θ of resources, but it
CR IP T
is too costly for them to recover the fraction θ of funds that the bank diverted. Accordingly, for creditors to be willing to supply funds to the bank, equation (26) must hold: the bank’s value if it honors the contract with his creditors must be greater than the value of diverting resources in the amount θJt Sjt and being shut down.
Bank j’s net worth, denoted Wjt ≡ Jt Sjt −Djt , is the critical state variable in the banker’s
AN US
problem. From equation (25), it evolves according to the following law of motion: Wjt = (RZt − Rt−1 ) Jt−1 Sjt−1 + Rt−1 Wjt−1
(27)
M
where RZt is the rate of return to one dollar invested in entrepreneurial claims: φ (π t + Jt ) Jt−1
(28)
ED
RZt ≡
As shown in Appendix G, when the constraint binds the bank’s asset holdings Jt Sjt are
Jt Sjt = Φt Wjt
(29)
CE
PT
constrained by its net worth Wjt , as follows:
where Φt is the maximum leverage ratio allowed by the incentive constraint, which is deter-
AC
mined recursively by the following set of equations: νt θ − µt ν t = Et Λt,t+1 1 − σ + σ(µt+1 Φt+1 + ν t+1 ) Rt µt = Et Λt,t+1 1 − σ + σ(µt+1 Φt+1 + ν t+1 ) (RZt+1 − Rt )
Φt =
(30) (31) (32)
Here, ν t is the expected discounted value of a unit of net worth, and µt is the expected 24
ACCEPTED MANUSCRIPT
discounted excess return on banks’ lending relative to the deposit rate. Note that the leverage ratio varies only with the aggregate state (and not with bank-specific variables) and depends positively on ν t and µt , and inversely on the parameter θ governing the severity of agency frictions.37 Entering bankers in period t receive a small transfer from households so they can start ξ f
of the value of assets in
CR IP T
operations. The transfer is assumed to be an exogenous fraction
the previous period, so that if bank j is a new entrant its net worth is given by Wjt =
ξ Jt−1 St−1 f
(33)
AN US
The problem of new entrants is thus identical to the one described above, but replacing the budget constraint (25) with Jt Sjt ≤ fξ Jt−1 St−1 + Djt .38
Aggregation.— Let St denote the aggregate quantity of securities and Wt aggregate bank Rf Rf net worth: St = 0 Sjt dj, Wt = 0 Wjt dj. Aggregating (29) across banks yields
M
Jt St = Φt Wt
(34)
ED
Aggregating Wjt across all banks (continuing ones and new entrants) and using (27) and (33) yields
PT
Wt = σ [(RZ,t − Rt−1 ) Jt−1 St−1 + Rt−1 Wt−1 ] + (1 − σ)ξJt−1 St−1
(35)
CE
The key source of fluctuations in aggregate bank net worth is movement in RZt , which arises from movements in the price Jt and in profits π t .
AC
Market clearing for entrepreneurs’ securities implies that the aggregate claims held by banks must equal the total number of existing varieties in period t, including those newly 37
Appendix G contains a detailed derivation of the banker’s problem. It is important that fξ > 0, so that newborn bankers have some initial equity capital. Otherwise, in light of (29), they would not be able to make any loans in their initial period. 38
25
ACCEPTED MANUSCRIPT
created during t: St = At + Zt . Therefore, equation (34) becomes Jt (At + Zt ) = Φt Wt
(36)
These considerations clarify how financial factors affect the evolution of TFP. When net
CR IP T
worth is low, through equation (36) the aggregate number of projects that the intermediation sector can finance is reduced, making “demand” for new innovations Zt lower. With a smaller Zt the growth rate of TFP will decline, as indicated by (18).
Frictionless financial markets.—Suppose there are no enforcement frictions, so that banks
AN US
are unconstrained. Perfect arbitrage then implies Et (Λt,t+1 RZ,t+1 ) = Et (Λt,t+1 )Rt = 1, or Jt = φEt [Λt,t+1 (π t+1 + Jt+1 )]
(37)
Without financial frictions, entry occurs until the value of entrepreneur equity, Jt , equals profits plus the price of equity in the following period, adjusted by the household’s discount
M
factor and accounting for obsolescence. In the absence of speculative bubbles, iterating (37) forward yields an expression for the equity price as the present discounted value of the
ED
expected stream of profits.
With financial frictions and constrained banks, we will have Et {Λt,t+1 (RZt+1 − Rt )} > 0,
PT
or equivalently
Jt < φEt [Λt,t+1 (π t+1 + Jt+1 )]
(38)
CE
The gap between the left- and right-hand side of (38) widens whenever banks’ financial constraints are tighter, which happens in times in when net worth of the intermediation
AC
sector is low. This implies that the rate of new firm creation falls below its frictionless level. The imperfection in financial markets also implies that the growth rate of TFP, and therefore of output, along the balanced growth path is below its value in a model without financial frictions.39 39
See Levine (1997), Levine (2005) for surveys of evidence suggesting that financial development has a positive impact on long-run TFP and output growth.
26
ACCEPTED MANUSCRIPT
3.4. Final Output and Intermediates Producers In each period t there exist a continuum of varieties of intermediate goods of measure At , indexed by i ∈ [0, At ]. The final good is produced by a competitive sector that uses the Z
Yt =
At
ϑ−1 ϑ
Yit
0
di
ϑ ϑ−1
CR IP T
different varieties of intermediates as inputs, by means of the production function
(39)
here Yt is the amount of final output produced, and Yit is the amount of intermediate i used in production. The representative final output producer chooses input quantities Yit RA for i ∈ [0, At ] by minimizing input costs 0 t Pit Yit di subject to producing a given level of
AN US
final output, where Pit is the price of variety i. Solving this problem yields a demand curve for each intermediate variety i:
Yit = where the price level Pt is
Z
Pit Pt
At
−ϑ
Yt
(40)
1 1−ϑ
(41)
Pit1−ϑ di
M
Pt =
0
ED
Each intermediate variety is produced by a competitive monopolist that employs a Cobb-
PT
Douglas technology with capital services Kit and labor Lit as inputs: Yit = Kitα L1−α it
(42)
CE
Intermediate goods firms face a working capital requirement that forces them to hold a quantity of non-interest-bearing assets at least as large as multiple θW of the quarterly wage
AC
bill:
κit ≥ θW Wt Lit
(43)
with θW ≥ 0, where κit denotes the amount of working capital held by firm i in period t. As shown in Uribe and Yue (2006) and Mendoza and Yue (2012), this formulation implies that the effective cost of labor is [1 + θW (Rt − 1)/Rt ] Wt (reflecting the opportunity cost of holding non-interest-bearing assets), and therefore an increase in the interest rate Rt raises 27
ACCEPTED MANUSCRIPT
the effective cost of labor. Working capital is assumed to be issued by a domestic financial intermediary (distinct from the bankers that fund entrepreneurs) which rebates profits to households as a lump-sum. The objective of producer i is to maximize profits π it : max
Pit ,Yit ,Kit ,Lit
Pit Yit − [1 + θW (Rt − 1)/Rt ] Wt Lit − rtk Kit Pt
(44)
CR IP T
π it =
subject to (40) and (42). The solution of this problem is standard, and details are relegated to Appendix H. All firms i set prices to a markup ϑ/(ϑ − 1) over marginal cost of production. Thus each firm i sets the same price and sells the same quantity, and therefore profits are
AN US
the same for all i. The Appendix shows how the following simple expression for profits in terms of aggregate output and technology can be obtained: πt =
1 Yt ϑ At
(45)
M
where the subindex i has been omitted from profits in light of the previous remarks. The expression above gives the payoff to entrepreneurs from renting a novel product design, via
ED
competition among potential producers of the new variety. As shown in Appendix H, combining (39) and (42) yields the aggregate production func-
1
Yt = Atϑ−1 (ut Kt )α L1−α t
(46)
CE
PT
tion
AC
RA where aggregate labor and capital services, Lt and ut Kt are given by Lt = 0 t Lit di and RA ut Kt = 0 t Kit di. Firms’ optimality conditions give rise to the following two equations: Yt ϑ = (1 − α) ϑ−1 Lt ϑ Y t rtk =α ϑ−1 ut Kt
[1 + θW (Rt − 1)/Rt ] Wt
(47) (48)
where use has been made of the aggregate production function (46). Marginal products of capital and labor are set to a markup ϑ/(ϑ − 1) over their respective marginal costs. 28
ACCEPTED MANUSCRIPT
3.5. Equilibrium and Aggregate Resource Constraint ∞ An equilibrium in this economy is defined as an exogenous stochastic sequence µwt , rtS t=0
and stochastic sequences for the eleven quantity variables { Ct , Γt , Lt , Bt , Kt+1 , Itn , ut , Zt ,
∞ At+1 , Nt , Wt }∞ t=0 , the four banking sector coefficients {Φt , µt , ν t , RZt }t=0 , and the seven ∞ prices Rt , Jt , PKt , rtk , Wt , WSt , π t t=0 such that: (i) The state variables {Γt , Kt+1 , At+1 , Wt }∞ t=0
CR IP T
follow the laws of motion given by (4), (9) with equality, (18), and (35); (ii) Households,
entrepreneurs, bankers, final output producers, and intermediates producers solve their optimization problems; (iii) Markets for entrepreneurs’ securities, skilled labor, regular labor, capital services, and final output clear.
By combining the budget constraints of bankers and households and using market-clearing
AN US
conditions, the following aggregate resource constraint can be obtained (see Appendix I for a derivation):
Bt − Rt−1 Bt−1 + Yt = Ct + Itn + δ(ut )Kt + Nt
(49)
M
where Bt is equals the sum of household and banking sector debt: Bt = Dt + Dt , with Dt ≡ Rf Djt dj. Equation (49) indicates that the economy uses output and international borrowing 0
ED
to finance consumption, investment in physical capital, and investment in innovation.
Given At , Kt , Γt−1 , Wt−1 , the twenty-two endogenous variables listed above are deter-
PT
mined by equations (4), (9), (11), (14)-(17), (18), (21)-(23), (28), (30)-(32), (35), (36), (45), (46)-(48), and (49). The economy features long-run growth in aggregate TFP, as well as in
CE
output and its components. The model is solved by first detrending appropriately to obtain a stationary system, and then computing a log-linear approximation around the steady
AC
state of that stationary system. Appendix J provides a list of the complete set of equations determining equilibrium and details on the detrended system. 4. Model Analysis This section presents numerical results from dynamic simulations of the model. The goal is to illustrate how the novel mechanism introduced in the paper—namely, financially constrained innovation—is able to produce the persistent effects of financial crises reported 29
ACCEPTED MANUSCRIPT
in Section 2. Section 4.1 describes the model’s parameterization and estimation. I use a limited information approach similar to Christiano et al. (2005) and Uribe and Yue (2006) to estimate key model parameters. The approach consists in choosing the parameters to minimize the distance between the effects of the Korean crisis estimated in section 2.2 and the analogous
CR IP T
objects in the model. In section 4.2, I perform simple experiments in the parameterized model designed to illustrate its workings. Section 4.3 discusses the model’s fit to the Korean crisis, and analyzes the role played by each of the model’s key mechanisms (financial frictions and endogenous innovation) in accounting for the Korean experience. Section 4.4 examines
4.1. Parameter Values and Estimation
AN US
the model’s performance over the business cycle more generally.
There are 19 parameters in the model, not including parameters related to the shock processes. I divide these parameters into two sets. The first set contains eleven standard parameters which I calibrate to conventional values. The second set contains eight parameters
M
which are estimated. I describe each set of parameter values in turn. Calibrated parameters.— This set consists of nine standard preference and technology
ED
parameters (β, , ρ, α, ϑ, θW , ˜δ, a, ψ, d), and also includes the transfer rate to starting bankers (ξ). Table C.1 in the Appendix shows their assigned values as well as a reminder of their
PT
meaning. I fix the quarterly discount factor, β, at 0.99, as in Gertler et al. (2007). Also following Gertler et al. (2007), I assume the wage elasticity of labor supply is equal to 2,
CE
and accordingly set = 1/2. This choice for the curvature of utility in labor is similar to the values typically used in studies of business cycles in small open economies, such as
AC
Mendoza (1991), Neumeyer and Perri (2005), Uribe and Yue (2006), and Mendoza and Yue (2012). The inverse of the intertemporal elasticity of substitution, given by ρ, is set at 2, also
a standard value in the literature (e.g., Mendoza (1991), Uribe and Yue (2006), Mendoza (2010)). I set the capital share α = 0.37 following Elekdag et al. (2006), who fit a small
open economy DSGE model to Korean data. The parameter governing the elasticity of final output with respect to intermediates, ϑ, is chosen so that the technological level At takes the purely labor-augmenting form, which from equation (46) amounts to imposing the restriction
30
ACCEPTED MANUSCRIPT
(1 − α)(ϑ − 1) = 1.40 This restriction implies that there exists a balanced growth path along which output is proportional to TFP, and therefore profits per period π t are stationary (see equation (45)). Given the choice for α, the resulting value for the intermediate goods markup is ϑ/(ϑ − 1) = 1.63, close to the value of 1.6 chosen by Comin and Gertler (2006). similar to the value estimated by Uribe and Yue (2006).
CR IP T
The working capital parameter θW is set to unity, following Neumeyer and Perri (2005) and Parameter ˜δ in equation (10) is set to ensure that depreciation is 2.5 percent each quarter along the balanced growth path, and adjust a so that capital utilization along the balanced growth path is normalized to unity. I set the elasticity of the country risk premium to aggregate foreign debt, ψ, to 10−5 . This value ensures that the dynamics of the model are
AN US
virtually unaffected by the debt-elastic risk premium at high and medium frequencies, while still making the foreign asset position revert to trend over the very long run. As usual in the literature, I assume the (net) country risk premium is nil along the balanced growth path, which implies that the foreign asset position relative to trend, B/A, is pinned down by the parameter d. I calibrate this parameter to match an average net exports to GDP
M
ratio of 1 percent, the Korean average in the decade prior to the crisis. The resulting value is d = 2.54. This value implies a ratio of B to Y of about 0.8 along the balanced growth
ED
path (an annual debt ratio of 20 percent). Finally, I set the new banker transfer rate to a very small value, ξ = 10−5 , which ensures that this parameter has no bearing on any of the
PT
model’s properties.41
Shock processes.— The first-order autoregressive coefficient ρR is set to 0.92, a value
CE
obtained by fitting an AR(1) process to quarterly data for the Korean country interest rate.42
AC
The standard deviation of the innovation rt is set to 0.20/100 so that the model matches the 40
Kung and Schmid (forthcoming) make a similar parameter restriction. As explained earlier, the purpose of a positive transfer rate ξ is to allow for some initial equity capital for entering bankers so that they are able to make loans in their initial period; otherwise, they would not be able to lend in the period they enter, which would preclude them from accumulating equity for subsequent periods. I verified that reducing ξ to even lower values has no effect on the results reported. 42 The estimated value for this autoregressive coefficient is sensitive to restricting the sample to the precrisis period (which yields about 0.86) as opposed to using the full sample, including the post-crisis (which gives 0.97). Because using only the pre-crisis sample likely understates the perceived persistence of the shock once it hit in late 1997, and because the pre-crisis sample is quite short, I set the autoregressive coefficient to the average across the two estimates. 41
31
ACCEPTED MANUSCRIPT
high-frequency component of fluctuations in the Korean country interest rate (see Section 4.4.) The value of the autoregressive parameter of the markup shock is set to ρµ = 0.88, following Comin and Gertler (2006). The standard deviation of the markup innovation is 0.76/100—a value chosen so that the model matches the high-frequency volatility of Korean GDP, as explained in Section 4.4.
CR IP T
Estimated parameters.— This set consists of eight parameters. Of these, three are preference and technology parameters for which there are no readily available estimates, or for which there is no clear consensus in the literature. These include: the parameter governing the impact of the total number of varieties At on labor disutility (γ), the elasticity of the price of capital to net investment (denoted ϕ),43 and the elasticity of marginal deprecia-
AN US
tion with respect to the utilization rate (δ). The remaining five parameters relate to the entrepreneurial and financial sectors. They include the survival rate of innovations (φ), the aggregate supply of skilled labor (LS ), the materials share in innovation (η), the banker survival rate (σ), and the divertable fraction of bank assets (θ).
To estimate this parameters, I first simulate a “crisis experiment” within the model,
M
meant to capture the Korean crisis. The disturbance that initiates the crisis is a shock to the country borrowing premium, Ψt , of 500 basis points, as in Gertler et al. (2007). The idea
ED
is to capture the fact that what precipitated the onset of the Korean crisis was a sudden and unanticipated outflow of capital, which is treated as exogenous.44 Because the shock
PT
in Korea occurred between late 1997:IV and early 1998:I, I model the increase in Ψt as two consecutive and equally sized innovations to rtS which lead to a total increase of 500 basis
CE
points in Ψt . As shown in Figure D.1, the model interest rate replicates the run-up in the Korean country risk premium well.
AC
Given the exogenous driving force, I then estimate the model’s parameters by minimizing the distance between the impulse responses in the model and the gaps between the actual In
The elasticity of PKt with respect to Ktt is ϕ ≡ −Υ00 (in ) in (see equation (16)). Given the assumptions on Υ(·) and the solution procedure used, no other features of Υ(·) need to be specified. 44 This is consistent with the view of Radelet and Sachs (1998), who argue that the crisis was triggered by a financial panic among international investors. On the other hand, Burnside et al. (2001) argue that the outflow reflected “prospective” fiscal deficits resulting from bailing out the banking sector. In my analysis, banking sector vulnerability plays an important role, but I continue to treat the increase in the country premium as exogenous. 43
32
ACCEPTED MANUSCRIPT
evolution of the Korean data post-1997:III and the projections calculated in Section 2.2. Let the subset of estimated model parameters be ε ≡ (γ, ϕ, δ, φ, LS , η, σ, θ), and let Ξ(ε) denote the mapping from ε to the model’s impulse responses to the country risk shock initiating b be the average gaps between actual and projected values. I use five the crisis. Let also Ξ b includes twenty elements years of data starting in 1997:IV (1998 for R&D and TFP), so Ξ
CR IP T
for each of the six quarterly variables and five elements for the two annual variables. The model counterpart of the credit spread in the data is given by the (expected) yield on loans to entrepreneurs relative to the riskless rate. It is computed assuming a maturity of three years (consistent with the maturity of the corporate bonds used in constructing the empirical credit spread) and expressed in annual terms.45
AN US
Then ε is estimated by solving
h i0 h i −1 b b min Ξ − Ξ(ε) V Ξ − Ξ(ε) ε
(50)
Here, V denotes a diagonal matrix with the min-max ranges of the estimated gaps along the
M
main diagonal. The weighting matrix V gives relative more weight to more precise estimates in the optimization problem. To avoid underweighting the annual variables relative to the
ED
quarterly ones, I multiply their weights times four. Table C.2 summarizes the resulting parameter estimates. The elasticity of the labor
PT
disutility term to aggregate technology is estimated to γ = 0.096—a small value, indicating that preferences are close to the GHH formulation and reflecting the sustained decline in
CE
labor input in the aftermath of the crisis. The investment adjustment cost parameter ϕ is estimated at 0.026, a value at the low end of the range reported by Bernanke et al.
AC
(1999), and in line with results in Uribe and Yue (2006). The latter authors estimate the parameter equivalent to the inverse of ϕ to be 72.8, implying a somewhat lower value of
ϕ than I find. The value for the elasticity of capital depreciation, δ, is very low, implying
a high responsiveness of utilization. This reflects the model’s attempt to generate the fast decline in measured TFP observed in the data. As shown below (see Figure 6), variable 45
Thus, the model credit spread is calculated as Et
nP 11
33
o [log(R ) − log(R )] /3 . Z,t+1+j t+j j=0
ACCEPTED MANUSCRIPT
utilization helps the model account for the near-term movement in measured TFP, with endogenous innovation accounting for the medium-run TFP decline. The range of values for this parameter found in the literature is quite wide, with Christiano et al. (2005) finding a very low value as I do.46 Turning to the parameters relating to the innovation sector, I estimate the variety survival
CR IP T
rate to be φ = 0.86, and the skilled labor supply to be LS = 0.15. These parameters help determine the time pattern of the TFP decline. The relatively high death rate of innovations partly reflects the fast decline in TFP observed during the Korean crisis. The estimated ratio of skilled labor supply to regular labor is about one sixth. The estimate for η is 0.19, suggesting a low materials intensity in innovation, and thus a high weight of labor in
AN US
the production of innovations—higher than in the production of intermediate goods. The resulting value of the ratio of materials used in innovation to output (N/Y ) is 6.8%, relatively close to the ratio of investment in intangibles to output for Korean manufacturing of 4.5% found by Midrigan and Xu (2014).
Turning to the financial sector parameters, I find that the estimation procedure drives
M
the banker survival rate σ to very low values. This reflects in part the very large, but relatively short-lived, increase in the Korean spread, which the model can generate via a low
ED
value of σ.47 To avoid implausibly low values of σ I set a lower bound for this parameter, equal to 0.9375 (implying an expected horizon of only four years, or equivalently an annual
PT
dividend payout of 4(1 − σ) = 25 percent of net worth). Once the bound is imposed, the estimation drives σ to its lower bound. The point estimate for the divertable fraction of
CE
assets is θ = 0.26. This value is within the range found in related studies—for example, Gertler et al. (2016) or Gertler and Kiyotaki (2015). Given these parameters, the steady-
AC
state credit spread equals 100 basis points, a value in the vicinity of the Korean spread in non-crisis times. 46
The point estimate for this parameter found by Basu and Kimball (1997) is unity, although the estimate is quite imprecise. 47 The credit spread in the model moves inversely with financial sector net worth. Low σ implies a high steady-state leverage ratio as well as low persistence of net worth. The former tends to make the spread more volatile, while the latter decreases its persistence.
34
ACCEPTED MANUSCRIPT
4.2. Simple Experiments I begin with three simple experiments designed to illustrate the behavior of the model. Figure 4 shows the model responses to three disturbances: a rise in the country premium Ψt (first row), a rise in the markup µwt (second row), and a shock to financial intermediaries’ net worth (third row). The figure shows the response of output Yt , along with three key variables
CR IP T
relating to the innovation and financial sectors: the aggregate number of firm varieties At , the R&D expenditure Rt , and the credit spread facing entrepreneurs. The last column shows the evolution of the corresponding forcing variable. In each case, the blue solid line shows the responses of the baseline model, and the green dashed line shows the behavior of the model without financial frictions (as described at the end of section 3.3 and in Appendix
AN US
J.2).
The rise in the country premium is a one-standard-deviation innovation in rtS (see equation (11)), leading the country interest rate to rise by 0.7 percent per year. From the blue solid line in the first panel, note that the shock has highly persistent effects on output,
M
which remains permanently below the unshocked path of the economy. Behind the behavior of output is the response of the aggregate number of varieties At , which drops persistently
ED
as a result of the sharp decline in R&D: intuitively, a reduction in the pace of innovation leads to a temporary decline in the growth rate of At , implying a permanent decline in its level.
PT
How does the model’s transmission mechanism operate? The initiating disturbance to Ψt leads to a decline in the value of entrepreneur equity Jt , as the stream of expected future
CE
profits per new intermediate good becomes lower and more heavily discounted. This has the effect of generating a large drop in the net worth of the intermediation sector, as equation
AC
(35) indicates. The shock leads the equity price Jt to fall much more in the baseline model than in the model without financial frictions, leading to a sharp rise in the credit spread. This is at the core of the model’s amplification mechanism: as banks’ constraints tighten— reflected in skyrocketing credit spreads—they are forced to cut back on project funding to a larger extent than would be the case with frictionless financial markets. Along the way, there is adverse feedback effect between net worth and the firm value Jt : as the former falls, banks’ constraints tighten, forcing a decline in the credit available for innovators. The decrease in 35
ACCEPTED MANUSCRIPT
demand for new projects leads to further drops in Jt , starting a new round of declines in bank net worth. The end result is a large decline in R&D expenditure—as made clear by the panel in the third column—which drops much more sharply in the baseline model than in the frictionless case. Overall, financial frictions play a quantitatively large role in leading to larger medium-run declines: aggregate technology At and output Yt fall about seventy
CR IP T
and fifty percent more, respectively, after five years in the baseline model compared to the frictionless model.
The second experiment is an increase in the markup µwt , resulting from a positive innovation to this variable of one standard deviation. As seen in the last column, the markup rises by 0.76 percent and then slowly reverts back to steady state. The rise in markups initiates
AN US
a decline in labor input and therefore in output, which drops about 1.6 percent after four quarters. As a consequence of the fall in activity, the profits from owning an innovation decline, setting off a drop in the asset price Jt which reduces intermediaries’ net worth and triggers the financial accelerator. Financial conditions tighten, with the credit spread rising almost sixty basis points. Technology At falls about 0.8 percent after 15 years—resulting
M
from the decline in R&D—which accounts for the fall in output over the same horizon. Note, however, that now output bounces back to some extent, relative to its short-run drop: the
ED
reason is that labor input recovers as the effects of the markup shock unwind. Still, the amount of medium-run damage is substantial, with output eventually recovering only about
PT
half the initial decline (0.8 versus 1.6 percent). By contrast, in the frictionless model the magnitude of the bounceback is much larger, about two thirds (1.5 percent drop initially,
CE
compared with 0.5 percent after 15 years). As explained above, at the core of the model’s transmission mechanism is the behavior
AC
of financial sector net worth. To illustrate this point, the third row of Figure 4 analyzes a transfer of wealth (e.g. via taxation) from financial intermediaries to households. In particular, I consider a one-time reduction of intermediary capital of one-third of its steady state value. As seen in the last column, this leads to a much larger decline in net worth on impact, due to the general-equilibrium drop in the asset price Jt . In addition, given σ > 0 the decline in net worth is persistent even though the transfer is purely transient, as equation (35) makes clear. The decline in net worth tightens intermediaries’ incentive constraints, 36
ACCEPTED MANUSCRIPT
which impedes their access to credit and leads to a rise in spreads. This then reduces the amount of financing available for entrepreneurs, leading to persistent losses in TFP and output. In the model with no financial frictions, by contrast, the shock has no aggregate consequences, as it simply represents a redistribution of wealth within the representative
CR IP T
household. 4.3. Accounting for the Korean Crisis
I now turn to comparing the theoretical impulse responses with the empirical gaps computed in section 2.2. Figure 5 shows the evolution of the model’s variables in response to the initiating shock (relative to the balanced growth path) along with their empirical coun-
AN US
terparts, from 1997 through the end of 2002.48 Overall, the model (blue circled line) does a good job of reproducing the empirical behavior of all variables following the crisis (shown in black). In particular, the model is able to capture the persistent gaps in the real variables observed in the data. The crucial element that allows the model to capture these mediumrun effects is the persistent decline in the aggregate number of firm varieties, At , in response
M
to the shock. This decline helps the model match the behavior of Korean measured TFP. At the same time, it depresses labor demand, leading to the persistent drop in employment. The
ED
behavior of the latter in the model also tracks its empirical counterpart well. The slowdown in aggregate technology At , along with the medium-run decline in inputs, then accounts for
PT
the persistent drop in output. In addition, the model also does a good job at capturing the empirical gaps of investment, consumption, and the trade balance. Most importantly,
CE
the model reproduces the sharp decline in business-sector R&D and the spike in the credit spread following the crisis—both at the core of the model’s transmission mechanism.
AC
Next, I compare the crisis experiment in the baseline model with the responses to the same shock in two alternative model versions: the frictionless model discussed at the end of section 3.3, and another version in which the endogenous growth mechanism is absent altogether. The results are included in Figure 5, with the responses in the frictionless model shown in green dashed, and the responses in the “exogenous growth” model shown in red dotted. 48
Figure D.2 in Appendix D compares model and data over a longer horizon.
37
ACCEPTED MANUSCRIPT
Financial frictions play an important role in amplifying the impact of the crisis shock. Without financial market frictions the credit spread is fixed at zero (as seen in the bottomright panel), and as a result the decline in R&D is much smaller. As a consequence, the real variables fall by much less: for example, the decline in output relative to trend four years after the shock is about 9 percent in the case without financial frictions, compared to 14
CR IP T
percent in the baseline model and in the data. The degree of amplification is even larger for TFP: by 2002 this variable is about 6 percent below trend in the baseline model (similar to the data), but is depressed by only 1.8 percent in the frictionless model. As explained in the preceding section, these differences are driven by the endogenous amplification implied by the interaction between the financial intermediation sector and the process of firm creation.
AN US
Figure 5 also reports the responses in a model in which the endogenous growth channel is inactive (red dash-dotted line)—what is then left is a standard RBC model of a small open economy with variable capital utilization and a working capital requirement, subjected to an interest rate shock. As made clear by the Figure, the overall declines in this case are much smaller in magnitude, and very far from the data—especially regarding the persistence of
M
the gaps—due to the absence of endogenous movements in At .49
Figure 6 provides more information on the behavior of TFP in the baseline model. Mea-
ED
sured TFP in the model can be decomposed into the contribution of the number of varieties 1
in operation and the contribution of capital utilization, as follows: TFPt = Atϑ−1 uαt . The
PT
figure shows the overall drop in measured TFP (the black circled line), along with the contribution of varieties (the blue bars) and the contribution of capital utilization (the red bars).
CE
Observe that the relative importance of the technology-driven component grows with the time elapsed since the shock: in 1998, the drop in measured TFP is largely accounted for by
AC
a drop in utilization, with technology explaining about one third of the overall decline. In contrast, by 2002 most of the weakness in TFP is accounted for by the technology component. 49
In fact, measured TFP in the exogenous growth model actually increases after two years, due to an increase in utilization. The reason is the following: utilization can be expressed as a monotonic increasing function of the L/K ratio. In the exogenous growth version of the model, employment starts recovering immediately after the shock hits, while the capital stock falls sluggishly—implying that the response of the L/K ratio eventually turns positive.
38
ACCEPTED MANUSCRIPT
4.4. Business Cycle Simulations This section examines the model’s performance over the business cycle. To this end, the Korean data is grouped into two sets: a set of real variables consisting of annual observations on GDP, consumption, investment, the net exports-to-GDP ratio, employment, businesssector R&D, and business-sector TFP, for the period 1975 through 2017; and a set of financial
CR IP T
variables consisting of quarterly series on the Korean credit spread from 1987:I through 2017:IV, and for the Korean country interest rate from 1994:I through 2017:IV. Appendix E provides details on data construction. As in Comin and Gertler (2006), I apply the bandpass filter (Christiano and Fitzgerald (2003)) to the real variables to extract medium-term fluctuations, where the medium term cycle is defined as including frequencies between 0 and
AN US
50 years. Figure D.3 in Appendix D displays the filtered data.50 The shorter sample size of the financial data complicates the task of extracting the medium-term cycle for these series, so I focus on the high-frequency component of these variables.51
Table 1 shows the standard deviations of the medium-term cycle, along with the high-
M
frequency (0 to 8 years) and the medium-frequency (8 to 50 years) components. Two features of the data are worth noting. First, the medium-term cycle is highly volatile: it features
ED
across-the-board higher standard deviations than the high frequency component, including (notably) for TFP. This is explained by the volatility of the medium-frequency component, which in most cases is at least twice as volatile as the high-frequency component (with the
PT
net exports ratio being one exception). Second, the usual volatility ranking across variables at the high frequency is preserved in the medium-term cycle: consumption is somewhat more
CE
volatile than output over the medium term, and investment much more so—between two and three times as much.
AC
Turning to the cyclical behavior of the real variables, consumption, investment, employ-
ment and particularly TFP are all strongly correlated with output over the medium term 50
I also experimented with filtering quarterly GDP, consumption, investment and NX/Y, which are available at the quarterly frequency (and starting in the 1960s). I found that the main statistics from the quarterly series are close to those calculated at the annual frequency, particularly regarding the medium-term cycle. Comin and Gertler (2006) reach a similar conclusion on the US data. 51 Applying the medium-term filter to the available financial series, I have found that these variables display much larger fluctuations at the high frequency relative to the medium frequency than is the case for the real variables.
39
ACCEPTED MANUSCRIPT
(Figure 7). Interestingly, R&D exhibits a significant lead over GDP over the medium-term cycle. It also displays a strong lead over TFP (see Figure D.4 in Appendix D), with a time pattern very similar to that with GDP. This fact is consistent with the evidence documented by Moran and Queralto (2018) for the US and for a set of advanced economies, who use VAR methods to show that increases in R&D tend to lead to increase gradual, persistent rises in
CR IP T
TFP.
Table 2 and Figure 8 document the standard deviations and cross-correlations with GDP of the financial variables. In each case, moments refer the quarterly high-frequency components. Two observations stand out: first, both financial series display considerable volatility at the high frequency, with a quarterly standard deviation of nearly 100 basis points in each
AN US
case. Second, both variables exhibit strong negative correlation with GDP. The country interest rate shows a strong negative comovement with (quarterly) output contemporaneously, while elevated levels of the credit spread lead adverse developments in GDP by several quarters (Figure 8).
Next, I turn to evaluating the behavior of the baseline model against the Korean data.
M
By construction, the model exactly matches the high-frequency standard deviations of the country interest rate and of GDP.52 The question is then how well the model can reproduce
ED
the volatility observed in the Korean data (particularly of the medium-term cycle), and whether it matches the cyclical comovement with output of the different variables.
PT
The model does a reasonably good job of replicating the standard deviations of the medium-term cycle. The model-based standard deviations are well within the confidence in-
CE
tervals for three variables (GDP, consumption, and investment), and just marginally outside for NX/Y and for TFP. On the other hand, the model underpredicts the volatility of R&D
AC
over the medium-term cycle by a substantial amount, while it gets closer to its volatility at the high frequency. One possibility here is that the evolution of Korean R&D may reflect “one-off” factors, as the country went from very low ratios of R&D to GDP in the 1970s to much higher ones in the 1990s and 2000s. Indeed, the standard deviation of medium-term R&D for the post-1990 period falls to about 16.9 percent, while the other variables’ standard 52
Country interest rate shocks explain about sixty percent of the high-frequency volatility of GDP, with markup shocks accounting for the remainder.
40
ACCEPTED MANUSCRIPT
deviations remain relatively unchanged when computed post-1990. The model also does quite well at capturing the medium-term cyclical comovements in the data. For most variables, the model cross-correlations lie mainly within the confidence variables. One variable for which the model is somewhat at odds with the data is the net exports-to-GDP ratio: the model predicts negative comovement between lagged NX/Y and
CR IP T
GDP, while in the data this variable does not exhibit strong cyclicality in either direction (the confidence intervals include zero throughout). Still, the contemporaneous correlation is similar in the model and in the data, at a small negative value. Countercyclical NX/Y in the model reflects the important role of country interest rate shocks in driving fluctuations. One relevant consideration here is that the volatility of the country interest rate is calibrated to
AN US
the post-1994 period, which may overstate the importance of interest rate shocks over the entire sample. A second aspect in which the model does less well is in matching the lead of R&D over GDP in the data, which the model captures to an extent (up to about 5 years, i.e. k = −5 in Figure 7) but not fully. One way to address this within the model is to add adjustment costs to R&D expenditure, as Moran and Queralto (2018) do.
M
Finally, the model does well at matching the standard deviations and correlations with GDP of the financial variables. In particular, it does a very good job of reproducing the
ED
high-frequency cross-correlation of the credit spread with GDP (Figure 8). Appendix D includes an additional experiment examining the model’s ability to reproduce
PT
the evolution of the Korean economy during the 2009 recession. The model can account for the 2009 episode quite well. The analysis reinforces the point that financial factors are crucial
CE
in generating a slow recovery: absent increases in the country interest risk premium and with financial frictions turned off, output bounces back much more from its initial decline, and
AC
the medium-run loss in TFP becomes significantly smaller. 5. Conclusion This paper has sought to account for the phenomenon of highly persistent output losses observed following financial crises. It has argued that such a phenomenon emerges naturally in a model in which innovation endogenously sustains trend productivity growth, and in which financial imperfections work to impede the smooth flow of resources to fund innovators’ 41
ACCEPTED MANUSCRIPT
investments. Thanks to these mechanisms, the model accounts well for the Korean experience following the 1997–98 financial crisis, as well as for the short- and medium-run fluctuations in the Korean data more generally. While the model’s main quantitative application is based on the Korean 1997 crisis, the 2008 wave of financial crises in advanced economies was also accompanied by extremely
CR IP T
persistent drops in activity, and productivity growth in these countries slowed significantly compared to previous averages.53 In several countries, including the US and the euro area, the crisis coincided with a period in which monetary policy was constrained by the zero bound. An interesting area for future research would be to explore how the mechanisms introduced in this paper might be used to address the experience of the advanced economies,
AN US
as well as the implications of the zero lower bound on policy rates in the type of environment analyzed here.54
Another potentially interesting application of the framework presented in this paper would be an evaluation of the welfare gains of government financial policy. Gertler and Kiyotaki (2010), Gertler et al. (2012) and Akinci and Queralto (2014), for example, analyze
M
different government interventions aimed at mitigating the adverse effects of financial crises, finding important benefits of intervention. The endogenous productivity growth mechanism
ED
introduced in this paper would likely affect what is at stake when considering intervention during a financial meltdown, and it could therefore have a substantial impact on the welfare
AC
CE
PT
gains of government policies directed at ameliorating the impact of a crisis.
53
See, for instance, the July 2017 Monetary Policy Report to Congress, p. 12-13. This avenue has been pursued in recent work by Benigno and Fornaro (2015) and by Moran and Queralto (2018). 54
42
ACCEPTED MANUSCRIPT
References Aguiar, M., Gopinath, G., 2007. Emerging market business cycles: The cycle is the trend. Journal of Political Economy 115 (1), 69–102. 1, 19
Finance Discussion Paper (1121). 5
CR IP T
Akinci, O., Queralto, A., 2014. Banks, capital flows and financial crises. FRB International
Anzoategui, D., Comin, D., Gertler, M., Martinez, J., 2016. Endogenous technology adoption and r&d as sources of business cycle persistence. 1, 4
Aoki, K., Benigno, G., Kiyotaki, N., 2009. Capital flows and asset prices. In: NBER In-
AN US
ternational Seminar on Macroeconomics 2007. University of Chicago Press, pp. 175–216. 5
Ates, S., Saffie, F., 2016. Fewer but better: Sudden stops, firm entry, and financial selection. 1
M
Ayyagari, M., Demirg¨ u¸c-Kunt, A., Maksimovic, V., 2012. Firm innovation in emerging markets: the role of finance, governance, and competition. Journal of Financial and Quanti-
ED
tative Analysis 46 (06), 1545–1580. 22 Bali˜ no, T., Ubide, A., 1999. The korean financial crisis of 1997—a strategy of financial sector
PT
reform. IMF Working Paper. 36
CE
Ball, L. M., 2014. Long-term damage from the great recession in oecd countries. National Bureau of Economic Research. 1
AC
Basu, S., Kimball, M. S., 1997. Cyclical productivity with unobserved input variation. 46 Benhabib, J., Rogerson, R., Wright, R., 1991. Homework in Macroeconomics: Household Production and Aggregate Fluctuations. Journal of Political Economy, 1166–1187. 29
Benigno, G., Fornaro, L., 2015. Stagnation traps. 54 Benjamin, D., Meza, F., 2009. Total factor productivity and labor reallocation: the case of the korean 1997 crisis. The B.E. Journal of Macroeconomics 9 (1). 1, 5 43
ACCEPTED MANUSCRIPT
Bernanke, B., Gertler, M., 1989. Agency costs, net worth, and business fluctuations. American Economic Review, 14–31. 1 Bernanke, B., Gertler, M., Gilchrist, S., 1999. The financial accelerator in a quantitative business cycle framework. Handbook of Macroeconomics 1, 1341–1393. 1, 4.1
CR IP T
Bianchi, F., Kung, H., 2014. Growth, slowdowns, and recoveries. National Bureau of Economic Research. 1
Bilbiie, F., Ghironi, F., Melitz, M., 2012. Endogenous entry, product variety and business cycles. Journal of Political Economy 120 (2), 304–345. 1, 35
AN US
Blanchard, O., Cerutti, E., Summers, L., 2015. Inflation and activity–two explorations and their monetary policy implications. Tech. rep., National Bureau of Economic Research. 15 Bruno, G. S., 2005. Approximating the bias of the lsdv estimator for dynamic unbalanced panel data models. Economics letters 87 (3), 361–366. 2.1
M
Buera, F. J., Moll, B., forthcoming. Aggregate implications of a credit crunch: The impor-
ED
tance of heterogeneity. American Economic Journal: Macroeconomics. 1 Burnside, C., Eichenbaum, M., Rebelo, S., 2001. Prospective deficits and the asian currency
PT
crisis. Journal of Political Economy 109 (6), 1155–1197. 44 Cerra, V., Saxena, S. C., 2005. Did output recover from the asian crisis? IMF Staff Papers,
CE
1–23. 18
Cerra, V., Saxena, S. C., 2008. Growth dynamics: the myth of economic recovery. American
AC
Economic Review 98 (1), 439–457. 1, 2.1, 2.1, 2.1
Christiano, L., Eichenbaum, M., Trabandt, M., 2015. Understanding the great recession. American Economic Journal: Macroeconomics 7 (1), 110–67. 2.2
Christiano, L. J., Eichenbaum, M., Evans, C. L., 2005. Nominal rigidities and the dynamic effects of a shock to monetary policy. Journal of Political Economy 113 (1), 1–45. 4, 4.1
44
ACCEPTED MANUSCRIPT
Christiano, L. J., Fitzgerald, T. J., 2003. The band pass filter. international economic review 44 (2), 435–465. 4.4 Comin, D., Gertler, M., 2006. Medium-term business cycles. American Economic Review, 523–551. 1, 3, 4.1, 4.4, 50
sources of output and asset price fluctuations. 1
CR IP T
Comin, D., Gertler, M., Santacreu, A. M., 2009. Technology innovation and diffusion as
Comin, D. A., Loayza, N., Pasha, F., Serven, L., 2014. Medium term business cycles in developing countries. Tech. rep. 1
AN US
Elekdag, S., Justiniano, A., Tchakarov, I., 2006. An estimated small open economy model of the financial accelerator. IMF Staff Papers, 219–241. 4.1
Furman, J., Stiglitz, J. E., 1998. Economic crises: Evidence and insights from east asia. Brookings Papers on Economic Activity, 1–135. 12
M
Gertler, M., Gilchrist, S., Natalucci, F. M., 2007. External constraints on monetary policy and the financial accelerator. Journal of Money, Credit and Banking 39 (2-3), 295–330. 1,
ED
4.1
Gertler, M., Karadi, P., 2011. A model of unconventional monetary policy. Journal of Mon-
PT
etary Economics 58, 17–34. 1, 23, 3.3
CE
Gertler, M., Kiyotaki, N., 2010. Financial intermediation and credit policy in business cycle analysis. Handbook of Monetary Economics 3 (3), 547–599. 2, 3.3, 5
AC
Gertler, M., Kiyotaki, N., 2015. Banking, liquidity, and bank runs in an infinite horizon economy. American Economic Review 105 (7), 2011–43. 4.1
Gertler, M., Kiyotaki, N., Prestipino, A., January 2016. Wholesale banking and bank runs in macroeconomic modelling of financial crises (21892). 4.1 Gertler, M., Kiyotaki, N., Queralto, A., 2012. Financial crises, bank risk exposure and government financial policy. Journal of Monetary Economics 59, 17–34. 2, 5 45
ACCEPTED MANUSCRIPT
Gopinath, G., Kalemli-Ozcan, S., Karabarbounis, L., Villegas-Sanchez, C., 2015. Capital allocation and productivity in south europe. National Bureau of Economic Research. 1 Gopinath, G., Neiman, B., 2014. Trade adjustment and productivity in large crises. American Economic Review 104 (3), 793–831. 5
CR IP T
Gorodnichenko, Y., Schnitzer, M., 2013. Financial constraints and innovation: Why poor countries don’t catch up. Journal of the European Economic Association 11 (5), 1115– 1152. 22
Greenwood, J., Hercowitz, Z., Huffman, G. W., June 1988. Investment, capacity utilization,
AN US
and the real business cycle. American Economic Review 78 (3), 402–17.
URL http://ideas.repec.org/a/aea/aecrev/v78y1988i3p402-17.html 3.1 Guerron-Quintana, P., Jinnai, R., 2014. Liquidity, trends, and the great recession. 1 Hall, B. H., 2002. The financing of research and development. Oxford review of economic
M
policy 18 (1), 35–51. 21
Hall, B. H., Lerner, J., 2009. The financing of r&d and innovation. Tech. rep., National
ED
Bureau of Economic Research. 21
Hall, R. E., 2015. Quantifying the lasting harm to the us economy from the financial crisis.
PT
NBER Macroeconomics Annual 29 (1), 71–128. 15
CE
Hayashi, F., 2000. Econometrics. Princeton University Press. 2.1 International Monetary Fund, 2009. World Economic Outlook, October 2009 Edition. 1
AC
Jaimovich, N., Rebelo, S., 2009. Can news about the future drive the business cycle? The American Economic Review 99 (4), 1097–1118. 28
Kehoe, T. J., Ruhl, K. J., 2009. Sudden stops, sectoral reallocations, and the real exchange rate. Journal of Development Economics 89 (2), 235–249. 5 Kerr, W. R., Nanda, R., 2009. Democratizing entry: Banking deregulations, financing constraints, and entrepreneurship. Journal of Financial Economics 94 (1), 124–149. 21 46
ACCEPTED MANUSCRIPT
Kiyotaki, N., Moore, J., 1997. Credit cycles. Journal of Political Economy 105 (2), 211–248. 1 Kortum, S., Lerner, J., 2000. Assessing the contribution of venture capital to innovation. RAND Journal of Economics, 674–692. 21
CR IP T
Krueger, A. O., Yoo, J., 2002. Chaebol capitalism and the currency-financial crisis in korea. In: NBER Conference on Preventing Currency Crises in Emerging Markets. University of Chicago Press, pp. 601–662. 12
Kung, H., Schmid, L., forthcoming. Innovation, growth, and asset prices. Journal of Finance.
AN US
40
Laeven, L., Valencia, F., 2013. Systemic banking crises database. IMF Economic Review 61 (2), 225–270. 2.1
Levine, R., 1997. Financial development and economic growth: Views and agenda. Journal
M
of Economic Literature 35 (2), 688–726. 39
Levine, R., 2005. Finance and growth: theory and evidence. Handbook of economic growth
ED
1, 865–934. 39
Mansfield, E., Schwartz, M., Wagner, S., 1981. Imitation costs and patents: an empirical
PT
study. The Economic Journal, 907–918. 33
CE
Mendoza, E. G., 1991. Real business cycles in a small open economy. The American Economic Review, 797–818. 4.1
AC
Mendoza, E. G., 2010. Sudden stops, financial crises, and leverage. American Economic Review 100 (5), 1941–1966. 1, 26, 4.1
Mendoza, E. G., Yue, V. Z., 2012. A general equilibrium model of sovereign default and business cycles. The Quarterly Journal of Economics 127 (2), 889–946. 1, 26, 3.4, 4.1 Meza, F., Quintin, E., 2007. Factor utilization and the real impact of financial crises. The B.E. Journal of Macroeconomics 7 (1). 5 47
ACCEPTED MANUSCRIPT
Midrigan, V., Xu, D. Y., 2014. Finance and misallocation: Evidence from plant-level data. American Economic Review 104 (2), 422–58. 1, 2.2, 20, 4.1 Moran, P., Queralto, A., 2018. Innovation, productivity, and monetary policy. Journal of Monetary Economics 93, 24–41. 1, 4.4, 54
CR IP T
Neumeyer, P. A., Perri, F., 2005. Business cycles in emerging economies: the role of interest rates. Journal of Monetary Economics 52 (2), 345–380. 1, 27, 4.1
Nickell, S., 1981. Biases in dynamic models with fixed effects. Econometrica: Journal of the Econometric Society, 1417–1426. 2.1
AN US
Oberfield, E., 2013. Productivity and misallocation during a crisis: Evidence from the chilean crisis of 1982. Review of Economic Dynamics 16 (1), 100–119. 1
Otsu, K., 2008. A neoclassical analysis of the korean crisis. Review of Economic Dynamics 11 (2), 449–471. 1
M
Pratap, S., Urrutia, C., 2012. Financial frictions and total factor productivity: Accounting
ED
for the real effects of financial crises. Review of Economic Dynamics 15 (3), 336–358. 5 Radelet, S., Sachs, J., 1998. The onset of the east asian financial crisis. Tech. rep., National
PT
Bureau of Economic Research. 12, 44 Reinhart, C. M., Reinhart, V. R., 2010. After the fall. National Bureau of Economic Research.
CE
1
Reinhart, C. M., Rogoff, K., 2009. This Time is Different: Eight Centuries of Financial Folly.
AC
Princeton University Press. 1
Ridder, M. d., 2017. Investment in productivity and the long-run effect of financial crisis on output. 21
Romer, P., 1990. Endogenous Technological Change. Journal of Political Economy, S71–S102. 1, 3, 3.2
48
ACCEPTED MANUSCRIPT
Sandleris, G., Wright, M. L., 2014. The costs of financial crises: Resource misallocation, productivity, and welfare in the 2001 argentine crisis. The Scandinavian Journal of Economics 116 (1), 87–127. 1 Stel, A., 2008. The compendia data base: Comparative entrepreneurship data for interna-
CR IP T
tional analysis. In: Measuring Entrepreneurship. Springer, pp. 65–84. 2.2 Uribe, M., Yue, V. Z., 2006. Country spreads and emerging countries: Who drives whom?
AC
CE
PT
ED
M
AN US
Journal of International Economics 69 (1), 6–36. 1, 26, 3.4, 4, 4.1, 4.1
49
CR IP T
ACCEPTED MANUSCRIPT
PT
ED
M
AN US
Figure 1: Impulse Responses to a Banking Crisis
AC
CE
Note: The figure reports impulse responses of output, hours, and productivity to a banking crisis, obtained by estimating equation (1). The black solid line represents the full sample, the orange dashed line restricts the sample to emerging market crises, and the blue dash-dotted line restricts the sample to advanced economy crises. Dark and light shaded regions indicate 1 standard deviation and 95 percent confidence intervals respectively, computed by drawing 100,000 realizations of the coefficients in equation (1) from a multivariate normal with covariance given by the estimated coefficient covariance matrix, and computing the impulse responses for each set of coefficients.
50
CR IP T
ACCEPTED MANUSCRIPT
PT
ED
M
AN US
Figure 2: Impulse Responses to Banking Crises and Other Recessions
AC
CE
Note: The figure reports impulse responses of business-sector R&D expenditures, TFP, and output to banking crises (blue solid line) and to recessions not associated with banking crises (red dashed line, labelled “Other Recessions”). Impulse responses are obtained by estimating (1). Shaded regions indicate 1 standard deviation confidence intervals calculated as described in note to Figure 1.
51
CE
PT
ED
M
52
AN US
Figure 3: The Korean Financial Crisis
CR IP T
ACCEPTED MANUSCRIPT
AC
Note: The figure reports Korean variables (blue solid line) around the 1997 crisis, along with the range of projections based on linear extrapolated pre-crisis trends (light blue shade). Grey vertical shaded areas indicate recession dates.
CR IP T
ACCEPTED MANUSCRIPT
Figure 4: Simple Experiments A
0
R&D
0 -0.01
1
-0.01
% p.a.
-0.01
log
log
Ψ
log
-0.02 -0.03
0.5
-0.04 -0.015
0
-0.05 -0.02
0
20
40
-0.02
60
GDP
0
0
20
40
-0.06
60
A
0
0
-0.01
40
60
GDP
0
0
-0.004
-0.004
60
% p.a.
0
20
40
60
R&D
20
-0.01
CE
0
40
quarters
60
0
20
40
log
60
-0.02
-0.04
0
60
0
20
40 quarters
Baseline
60
40
Markup
0.004 0.002
0
20
40
0
60
Credit Spread
0
20
40
60
Net Worth
0
-0.2
-0.4
-0.6
0
20
40 quarters
60
-0.8
0
20
40 quarters
No financial frictions
Note: The figure shows impulse responses to country premium (top row), wage markup (middle row), and net worth (bottom row) shocks, for the baseline model (blue solid) and for the model without financial frictions (green dashed).
AC
60
0.006
0.5
-0.5
20
0.008
0
quarters
0
0.01
1
-0.03
-0.008
40
Credit Spread
1.5
-0.006
-0.008 -0.01
-0.2
-0.01
log
log
-0.006
-0.04
0
PT
-0.002
40
A
0
-0.002
20
20
0.2
0
% p.a.
20
0
0.6
-0.03
ED
0
-0.015
-0.02
M
log
log
log
µw
-0.02
Net worth
53
-0.01 -0.015
-0.5
60
0.4
-0.005 -0.01
40
R&D
0
-0.005
20
0.4
0.2
AN US
-0.015
0.6
% p.a.
-0.005
Country Interest Rate
0.8
log
-0.005
Credit Spread
1.5
log
GDP
0
60
CR IP T
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
54
AN US
Figure 5: Korean Crisis, Model v. Data
Note: The figure shows the data average deviations from pre-crisis trends (black solid) and the min-max range deviations (grey shaded area), along with the simulation corresponding to the baseline model (blue solid line with circles), the model without financial frictions (green dashed line), and the model with exogenous growth (red dotted line).
CR IP T
ACCEPTED MANUSCRIPT
Figure 6: Baseline Model, TFP Decomposition
AN US
0 -0.01 -0.02 -0.03
M
-0.04
-0.06 -0.07
Model: Measured TFP
Contribution of Varieties: A 1/(ϑ-1) Contribution of Utilization: u α
PT
-0.08
ED
-0.05
1997
1998
1999
2000
2001
2002
AC
CE
Note: The figure reports model measured TFP in the crisis experiment (black circled line) along with the contribution of technology (blue bars) and of capital utilization (red bars).
55
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
Figure 7: Cross-Correlation with GDP, Medium-Term Cycle
Note: Each panel shows the medium-term-cycle correlation between GDP at time t and the corresponding variable at time t − k in the data (black dotted line, 95-percent confidence bands indicated by the shaded area) and in the baseline model (blue circled line). Data confidence intervals obtained using the Newey-West estimator.
56
CR IP T
ACCEPTED MANUSCRIPT
PT
ED
M
AN US
Figure 8: Cross-Correlation with GDP, High-Frequency Component
AC
CE
Note: Cross-correlations between the high-frequency component of the credit spread (left panel) and of the country interest rate (right panel) at quarter t − k with the high-frequency component of GDP at quarter k, in the data (black dotted line, with 95-percent confidence bands indicated by the shaded area) and in the model (blue circled line). Data confidence intervals obtained using the Newey-West estimator.
57
CR IP T
ACCEPTED MANUSCRIPT
Table 1: Standard Deviations, Annual Frequencies
GDP Consumption Investment NX / Y
Model
6.45 (5.08–7.81) 7.63 (5.76–9.51) 15.09 (11.51–18.67) 3.19 (2.28–4.10) 2.64 (1.88–3.41) 22.94 (18.56–30.39) 4.83 ( 3.11–6.55)
6.22 6.12 17.50 4.32 4.64
ED
Employment
Data
R&D
PT
TFP
9.60
2.75
Medium-frequency component 8-50
Data
Model
Data
Model
2.06 (1.40–2.72) 2.58 (1.35–3.81) 6.87 (4.49–9.25) 2.00 (1.23–2.77) 1.11 (0.64–1.58) 8.53 (4.62–14.73) 1.42 (0.95–1.89)
2.06
6.01 (4.70–7.33) 7.15 (5.54–8.76) 12.89 (9.22–16.55) 2.43 (1.46–3.41) 2.37 (1.64–3.10) 20.68 (15.43–28.22) 4.60 (3.11–6.09)
5.77
AN US
Variable
High-frequency component 0-8
Medium-term cycle 0-50
M
Frequency
2.50
7.96
2.43 1.84 5.27 0.84
5.46 15.16 3.44 4.16 7.78 2.57
AC
CE
Note: Standard deviations in the model and in the data of the medium-term cycle (frequencies between 0 and 50 years), the high-frequency component (0 to 8 years) and the medium-frequency component (8 to 50 years). Data 95-percent confidence intervals (computed using the Newey-West estimator) shown in parenthesis. Model standard deviations computed by averaging 10,000 simulations of a sample size corresponding to the data.
58
CR IP T
ACCEPTED MANUSCRIPT
Table 2: Financial Variables, Quarterly Frequencies (High-Frequency Component 2-32)
Credit Spread
Data
0.93 (0.47 – 1.40) 0.89 (0.64 – 1.14)
1.46
Data
Model
-0.28 (-0.45 – -0.10) -0.52 (-0.85 – -0.19)
-0.30
Country Interest Rate
M
Contemporaneous Correlation with GDP Credit Spread
ED
Model
AN US
Standard Deviation
PT
Country Interest Rate
0.89
-0.27
AC
CE
Note: Standard deviations and contemporaneous correlation with GDP of financial variables, in the model and in the data. Data 95-percent confidence intervals (computed using the Newey-West estimator) shown in parenthesis. Model moments computed by averaging 10,000 simulations of a sample size corresponding to the data.
59